Data as a Service: The What, Why, How, Who, and When

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Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are the key to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service.

Buckley Barlow Jonathan Greene

Buckley Barlow & Jonathan Greene
Founding Partner & CEO | RocketSource

Post Summary

  1. How to Turn Data Into Profits
    Monetizing data means gaining deep enough insights to fuel your organization's growth on the S-curve of Business.

  2. What Happens in the Data as a Service Process?
    Data as a Service involves data looping, data visualization, and data mapping. Each are critical components to helping you reach a bigger goal.

  3. The Advantages and Challenges of DaaS
    There are tremendous benefits to working with a DaaS provider, but if you're not careful who you choose, there can be some major challenges as well.

  4. Incorporating a Third Party With Your Team
    When it comes to incorporating a DaaS provider with your team and business intelligence, there's a specific strategy to follow to protect your data.

When people think of Data as a Service or DaaS, they often conjure up images of complex algorithms and machine learning, but what we at RocketSource think of first is making data sets accurate, easy-to-digest, and actionable. That’s not an easy task. There’s a firehose of information pouring into businesses, and yet, our years of experience as a digital consulting firm have shown us that executives continue to struggle to harness it all. There’s tremendous confusion about how to use data to drive buy-in, fuel business transformation, and drive top and bottom line ROI-generating initiatives.

You’ll be hard-pressed to find an executive in today’s world that doesn’t appreciate the importance of data-centric insights. And yet, many executives are left scratching their heads wondering how to collect it, mine it, visualize it, humanize it, and most importantly, act on it.

Data analytics are far more complex than setting up algorithms to feed into databases. To tap into the insights buried in datasets in a meaningful way—one that yields tangible results—requires both human touch paired with a scientific approach. In this post, we’re going to dig deep into the ambiguous field of Data as a Service.

Quick reading note: We are Buckley Barlow and Jonathan Greene, co-founders of RocketSource and we’re writing this post shoulder-to-shoulder in an effort to apply our collective skill sets. Our goal is to give you as much insight on Data as a Service as possible. Although it’s co-authored, we want this to feel personal, so be sure to hit play on each of the audio soundbites in the post where you’ll hear us personally dive in deeper to this rich topic.

Data as a Service on the Gartner Hype Cycle

If you’re anything like us, you aim to avoid risks associated with shiny object syndrome, such as paying too much for new services that won’t stick around for the long run. We’re firm believers that investments should pay off in dividends, which is why we regularly look to maturity indexes when making decisions for our business and for our clients. For example, we lean heavily on the S-curve of Business when analyzing the maturity and evolution of a company. Though the S-curve is a great way to plot and visualize a business cycle, as well as current vs. future state, we leave it to Gartner and other leading research firms to speculate on the latest and greatest technologies and how they will evolve. For this Data as a Service analysis, we keep a regular pulse on Gartner’s hype cycle for data management.

In brief, the Gartner hype cycle showcases which technology is worthwhile to adopt and the timeline in which you should consider adopting it. The first part of the curve—the highest peak—showcases the areas filled with hype from the media. The prospects listed here are new and exciting, but also unfamiliar because there’s such little adoption in the marketplace. That lack of adoption means that the risks are relatively unknown. As a technology moves through the hype cycle, the costs and benefits become clearer and more defined, which in turn makes these solutions less risky to adopt. Some technologies will move quickly through as adoption picks up steam, whereas others will stall out in the Trough of Disillusionment.

Take a look at where Data as a Service sits in this recent Gartner hype cycle.

Data as a Service on the Gartner Hype Cycle

Source: Gartner (September 2017)

You can see that Data as a Service is on the rise but Gartner deems it still 5-10 years from the Plateau of Productivity, or where it’s estimated that high-growth adoption will kick in. This tells us that DaaS has some serious staying-power, which is no surprise due to its ability to tap into journey analytics and humanize big data and offer unprecedented glimpses into consumer and employee behavior. But for now, DaaS is sitting comfortably on the slope upward of positive media hype. It’s still early enough on that many corporations are unsure about the costs and benefits. It’s that ambiguity that we hope to clear up in this post, so let’s get to it.

We hope you’ll find our break down of DaaS fascinating even if you don’t read about R-Squared or Python for fun (but especially if you do). Fascinating or not, our post will take you through how to mitigate disasters, gain buy-in, and refine business strategies using data-driven insights.

What is Data as a Service?

What role does Data as a Service play in today's data overload?

Jonathan Greene | CEO at RocketSource

Jonathan Greene | CEO at RocketSource

Data accessibility requires the right infrastructure, proper handling, scientific and statistical expertise, and creative strategy. This combination takes a great deal of knowledge, time, talent, and infrastructure. To make this data available organization-wide, regardless of geographic location and without draining capital budgets, many companies deploy a cloud-based strategy known as Data as a Service (DaaS).

Before we dig too deep into DaaS, let’s rewind to a more familiar “as a service offering”—Software as a service (SaaS). You probably use some type of SaaS product right now in your organization, such as a customer relationship management tool like SalesForce, or an email marketing service like ConvertKit. Since SaaS products were introduced in the 1990s, a slew of businesses have taken root both in SaaS and in the “as a service” family. A relatively recent and notable addition to these offerings is DaaS.

Data as a Service wrangles the overload of information out there today and makes it available across all departments anytime, anywhere. Like anything in the “as a service” family, DaaS providers serve up its data-centric insights through the cloud securely and affordably. But DaaS isn’t a set-it-and-forget-it service that you can log into whenever you have a spare moment and instantly get access to a list of data-driven insights. There’s a lot of strategy, science, and structuring that goes into democratizing datasets so that they’re understandable and actionable.

How Data as a Service Providers Simplify the Complex

Analytical analysis doesn’t just sound complex, it is complex, which is probably why so many people choose to keep their heads in the sand about how to wrangle all the data that’s available. The more data at your disposal, the more important advanced analytical analysis becomes. And yet, interpreting data is just one challenge. Before businesses can even get to that obstacle, there are other hurdles they have to conquer first.

Late last year, Retail Systems Research surveyed retailers worldwide and found that many organizations struggled to tap into the data sets at their disposal. The reasoning wasn’t solely because they didn’t have the expertise (although that was the case for 46% of respondents). 56% of respondents said it was because they don’t have the bandwidth, 38% said their analytical engines couldn’t handle everything coming in, and 25% didn’t have anywhere to put the data once it was collected. Those aren’t hurdles in analysis. They’re hurdles in operations and infrastructure, which are surprisingly common among companies striving to collect and crunch data independently.

daas-leading-challenges

As you can see from the findings showcased in this image, the lack of infrastructure is enough to stunt over half of the surveyed companies from running an intelligent operation. Without your organization being built around the ability to process data, you won’t have the tools you need to properly collect, store, and clean data to ensure you’re gleaning the most accurate insights.

But having the infrastructure alone isn’t enough. You also need to know where you have data available and how to tap into it. In the study above, you can see that 37% of companies didn’t know where to begin to gather data. And, a recent Accenture study found that 80% of organizations are sitting on unstructured and inaccessible data. Unstructured data refers to pictures, videos, social media feeds, content online, handwritten feedback, and other similar elements. Sound familiar to your own organization? That lack of access to data could be costly because without knowing where or how to look for insights, you’ll sit on massive and expensive data sets that aren’t actively contributing to growth opportunities.

In fact, businesses can often have such an inefficient approach to wrangling data that Data as a Service providers may need to slightly pivot a company’s business model in order to collect cleaner data more efficiently. For example, the DaaS provider may need to partner up with outside sources to ensure the data coming in is clean and can be merged with other data sets. Or, the DaaS provider might alter the way data is collected and distributed organization-wide by adjusting collection methods or refining the design and content of surveys being distributed. Of course, all of this has to happen while falling under General Data Protection Regulation (GDPR) compliance .

There are numerous ways that we, as DaaS providers, tap into datasets and use that information to draw out actionable insights. Here’s a small sample of some of the ways a DaaS provider supports a client.

Data as a Service (DaaS) Solutions

As you can see, this service goes beyond building algorithms to fill your data lakes. Data science requires that you tap into the analytical and scientific side of your brain to understand complex data sets. DaaS providers are tasked with wrangling big data sets and taking them through heavy analytical rigor. There’s science involved in how the data is collected and aggregated. There’s strategy to how you run deep analyses while looking at statistical significance and identifying correlations. But there’s also an art to pulling out insights from advanced analytics and visualizing them in a way that’s digestible by everyone company-wide, regardless of education or experience.

This blend of science and art requires a unique approach. The right DaaS providers take a page out of every startup’s book and use complex data to come up with creative solutions rooted in intelligent analytics. Sounds counterintuitive, but stick with us on this point. Startups typically have a product that a handful of people have adopted with eager excitement. Often times, it’s those small handfuls of people that provide motivation for the startup to create a product based on a gut feel of what the consumers want. That approach certainly isn’t rooted in the limited data on hand—it’s rooted in vision.

DaaS providers take a similar approach but apply intelligent analytics to the vision to find quick wins and low-hanging fruit. These insights enable the organization to find growth opportunities, plan execution paths to adopt new initiatives and innovate in a way that will have the greatest impact while being timely and cost-effective. This approach is done quickly, like you typically see done at the startup level, but without the risk that typically comes from running an agile organization.

The primary focus of a DaaS provider is to get enough information to validate assumptions and gut reactions to the market. This validation is used in conjunction with vision to enhance the approach that’s so often seen in the startup world. A DaaS provider will then take that data collection a step further and draw out insights that can be visualized and mapped out into action steps for the organization. The goal of all of this? To transform big data sets into profit-building, intelligent initiatives. Monetization is a big deal and a huge hurdle, so it’s worthy of a closer look.

Data as a Service Delivers a Framework for Monetizing Big Data

Do you have data but aren’t sure how to monetize it? If so, you’re not alone. One of the biggest challenges we hear time and again from executives is the lack of concrete plans for how to turn hard data into actual revenue.

Data wrangling, data tuning, data mining, and data lakes are common buzzwords but they’re only a portion of the Data as a Service offering. By focusing exclusively on building strategies around those buzzwords, you lose out on a big opportunity—the chance to turn the data you’ve gathered, mined, and stored into profitable, growth-driving initiatives. To see the type of revenues available in from those rich data sets, you must adopt a larger data-centric, strategy.

You can have data without information, but you cannot have information without data. — D.K. Moran

When we talk about generating revenues from data, we aren’t talking about selling datasets for cash. Data monetization goes far deeper than that. It’s about collecting and packaging data insights in a way that delivers and enables best-in-class outcomes. This process isn’t about turning data into dollars, but rather turning data into irrefutable insulated value and exchanging that value for legal tender or perceived equivalent value.

Our data looping and mapping process is second to none here at RocketSource and that’s because we go after root-cause analysis to uncover the cognitive associations to your customer’s motivations and behavior and how that cognitive association matches the contiguous and overarching customer journey and customer experience of your brand. We don’t start with KPIs and North Star Metrics. Instead, we monetize initiatives by fully understanding how they fit within the overarching purchase behavior.

We have found that, through our experience, many business challenges can be analyzed and optimized by gaining a wider perspective. It’s through this wider perspective that we can address the root-cause problems and deal with them accordingly. By taking this approach, we’re able to frame the metrics directly related to spending and business outcomes, which enables us to better showcase the value achieved and then correlate them with the key ROI drivers for our client.

Bringing in the data to your organization is only a small part of what it takes to fuel growth, and thus drive more cash flow into your business. In order to see the return from the thousands or millions of dollars spent setting up these robust systems, you must have a plan in place to harness the data you collect from your customers and use it to derive better experiences with your brand. These better experiences are the ultimate point of arrival because they’re what will set your business apart from the competition. It’s that differentiator that will enable you to get closer to seeing hockey-stick style sales charts.

Improving customer experience initiatives requires that you have some layers in your business to make your company more intelligent. One of those layers is data analytics.

Data analytics has been shown to strengthen customer experiences both client-facing and in B2B industries. According to research by Econsultancy and Adobe, 65% of respondents said that data analysis was very important to improving customer experiences for client-facing marketers and 41% of B2B professionals said the same thing.

Benefits of Data as a Service

The data here shows that one of the benefits of Data as a Service are improved customer experiences. When we look at monetizing data fronts, it’s about finding ways to increase on various metrics that are important, not only to build on customer experiences, but to build the business as a whole. Onboarding, churn rates, time-on-site, and scroll depth are all examples of engagement metrics that are critical for businesses to watch. The more engagement you have, the more involved the consumers feel, and the more consumers are driven to return to make repeat purchases from your brand. And that engagement is a signal of something bigger—positive customer experiences.

According to Accenture, more and more consumers are turning off personal data taps, which has made it harder for companies to gather the data needed to improve experiences. Accenture’s data shows that 44% of the US consumers are frustrated by companies lack of personalization in their service. Still, 49% of consumers said they were concerned with personal data privacy, leading them to opt out of personal data taps. For companies to continue to tap into data to drive experience initiatives and grow sales, they need to get creative in how they’re collecting data, while simultaneously mastering how they use the data available to drive better experiences.

Experience initiatives rooted in intelligent operations are one of the best investments you can make with your data. — Jonathan Greene

Poor customer experience is the bane of monetizing data. A lack of quality personalized experiences, paired with consumer distrust, cost businesses $756 billion in lost retail and brand sales in 2017.

One of the best ways to avoid poor customer experiences is to keep a pulse on what’s happening in the market. If you’re like us and many other forward-thinkers, you’re probably a regular reader of reports like Mary Meeker’s 2018 Internet Trends report. In the most recently released version, she backs up our conviction that customer experience is paramount using her own data findings. Here’s the full presentation for you to sift through.

If you don’t feel like scrolling through 294 slides right now, we can’t blame you, though it’s worth bookmarking and returning to. In the meantime, here are some of our favorite takeaways as they pertain to using data, personalization, and the customer’s experience.

  • Search terms with “near me” have grown 900% showcasing the demand by consumers for personalized results.
  • Deloitte’s research, cited in this report, found that 64% of American consumers delete apps because they’re worried about data privacy.
  • Yet Deloitte’s study also showed that 79% of American consumers are willing to share personal data when it involves a ‘clear personal benefit.’
  • Mary Meeker included one notable business that’s accomplishing this goal of getting consumers to share data and using that to deliver a clear personal benefit on slide 79—Stitch Fix.

Stitch Fix is a subscription box that delivers clothing on-demand to consumers across the country. The service offers a unique experience by personalizing the clothing in each box to the person’s taste preferences and individual needs. The personalization isn’t done through guesswork or purely through human analysis. Stitch Fix reveals that it’s driven by 85 data points the customers willingly provide, such as style, size, fit, and price preference. In addition to any details you’d need if you were to act as a personal shopper for someone, Stitch Fix also collects offbeat details, such as areas of the body where clothes typically fit awkwardly, or areas that the person would like to flaunt or cover up. Once that data is crunched, one of Stitch Fix’s 3,400 stylists gets to work adding their human touch and personalized input when selecting the pieces. The end result is an experience that feels personal and reminiscent of the days when store tailors greeted you by name and took individual measurements, but is driven by data.

We are fascinated by Stitch Fix’s data-driven strategy and clearly so was Mary Meeker because she included this on-demand clothing company in her report two years in a row. In the 2017 Mary Meeker digital trends report, she highlighted how Stitch Fix is pairing data science with personal fashion preferences by highlighting the clothing empire’s computer generated designs based on customer feedback, product attributes, and data science.

daas mary meeker 2017

This use of artificial intelligence to humanize data looks powerful even on the surface, so we wanted to dig deeper into what this type of service has done to Stitch Fix sales. As we guessed, this strategy has proven successful in today’s day-and-age where shoppers want on-demand, personalized services. Although subscription box services saw their online traffic decline by 3% in 2017, Stitch Fix’s traffic more than doubled in that period because of their ability to reinvent the shopping experience through personalization.

data-as-a-service-stitchfix-ai

Stitch Fix’s success is just one example of the importance of having the full package in place—data and personalized service. That personalization has proved paramount in the otherwise impersonal ecosystem of online shopping. Looking at McKinsey’s 2018 state of the subscription economy, you can see why.

Personalized Experiences

As you can see from these radar graphs, personal recommendations was one of the top three reasons for people to initiate a subscription. Once subscribed, personalized experiences was the top consideration when deciding whether to continue a subscription. It’s up to the subscription services to deliver an exceptional personalized experience to keep people coming back, and recommending the service to others.

This expectation for personalization isn’t exclusive to subscription models either. Customers today demand personalized interaction online, offline, and regardless of industry. But personalization is hard to scale. At least, it has been until recently. Now, with artificial intelligence leading to data-driven insights, companies can deliver the personalized experiences that keep people engaged and buying time and again.

These are the type of insights DaaS providers help uncover that will enable businesses to grow profit margins by implementing strategies, such as delivering more personalized experiences. To get started gathering these insights, we use data looping.

Data Looping

Data looping is a systematic way to collect information, pull out insights, and then put those insights into motion. To do this effectively, you’ll need the right people in the right seats on the data bus.

Before we get too far here, we want to address something that tends to weigh heavy on people’s hearts—the fear of job loss as a result of all of the technology used to gather data and draw insights. This fear is not new to our society. In the 1700’s (yeah, we’re going that far back), it used to take 10 people to do the job that a backhoe could do in half the time. What a world. During that time, people were worried about what the backhoe and other industrial machines would do to their jobs. Were machines taking over the world? Not quite. Unemployment rates stayed relatively low as those people got reassigned to other areas that needed the human touch. Calculations suggest that during the Industrial Revolution the unemployment rate only topped out around eight percent per year, but could have been much lower. Moreover, quality of life and wages increased, making the revolution just that—a revolution in how we operate as a society.

The same is true today of data and analytics. It seems that more and more people are worried about what machines will do to human jobs. We would argue that the era of big data isn’t a question of job loss but rather a question of job description. Ravin Jesuthasan and John Boudreau at the Harvard Business Review have an article about how automation makes us rethink jobs. In the article they say: “Deconstructing and then reconfiguring the components within jobs reveals human-automation combinations that are more efficient, effective, and impactful.” Even though machines might seemingly be threatening to take over our work, what they’re really doing is opening up new opportunities for jobs that still require human thought. We’re a long time away from seeing a replacement for human empathy and the need for human intervention, which means that regardless of how automated our world becomes, human touch will still be a necessity.

To showcase Jesuthasan and Boudreau’s point, let’s look specifically at the data looping process. This is the process by which we collect the data. That data looping process requires human expertise paired with the power of automation to give us the deep, rich insights needed to then go on and develop impactful, actionable plans. As mentioned earlier, Data as a Service isn’t a simple offering. One part of the service is to set up a data loop, and doing that requires the skill and expertise of a variety of people in a variety of roles.

The Roles Within Data as a Service

Here you can see eight roles in the data looping process, each of which require collaboration across a variety of departments. The process starts with designing and crafting content for the methods with which you’ll gather data. To do this effectively, you must have a designer on hand who understands how to design the data-collection process, so that it’s seamless for the customer and honors the brand’s image. Otherwise, the person on the other end will have a jolted experience while you gather information. The person, or people, writing the content should be researchers and analysts who understand how to ask questions in a way that results in clean, honest, and deep answers. This step requires careful maneuvering to avoid swaying the response to try to appeal to what the company wants to hear.

Once you’re ready to deploy your data collection, you need a systems administrator and software engineer to handle the technical aspect of the deployment and a database administrator to put the answers in the right databases. Then, a data scientist typically gets involved to help aggregate all of that data from the various sources.

As you move into the mining and modeling phase, the data scientists will run a correlation analysis or a regression analysis. In this stage, you may also choose to put your data into a third-party analytics tool, such as Alteryx, to start mining insights. These people and/or tools will be tasked with verifying that there’s enough statistical significance to validate the findings and start determining what’s driving the answers.

The first step to building out your journey analytics requires that you have a system of gathering both qualitative and quantitative data. But gathering data alone isn’t enough. For example, if you’ve built a Hadoop and you’re using that to go and centralize disparate databases, you have that infrastructure, but that’s only a part of the data looping process. To glean insights, you need to roll the data back in and close the loop.

If you’re just collecting the data and not closing the data loop, you’re in trouble. Silo walls start building, and soon departments pull back on communicating with one another on important initiatives. This lack of cross-departmental communication can lead to teams working independently of each other, which can make experience initiatives dead on arrival.

Letting valuable insights lie dormant is a common failing, and the way to avoid it is to visualize your data and relate them to your business’s why. Until your data tells a complete and relevant story, you’ll struggle to be able to pull strong insights and monetize it. It’s here, in the last step of data looping, that having a designer put their skilled hands back on these insights will enable you to bring these findings to the rest of your organization and breaking down those silo walls.

Data Visualization

The final step in the data looping process is data visualization. This is where the DaaS provider showcases your data so that it tells a story and enables you to gain buy-in from your team, investors, or executives.

Data as a Service turns complex data into something more viable and accessible that can be used to make a massive impact on the bottom line. — Buckley Barlow

Effective data visualization enables you to democratize the data gathered in your data loop. What does democratizing data look like? It involves making the insights understandable across all departments, teams can access the insights they need and operate through the same lens. This approach will enable you to showcase your ideas while validating them with sound data.

Getting this buy-in across all departments goes beyond adding color to a chart. The ability to tell a story and get the right data into the right models at the right time is one of the biggest challenges organizations face.

With so many options for charting data points available, such as radar graphs, bubble charts, and stacked datasets, it takes a creative mind to know what to use and what will tell the strongest story to gain that buy-in. But we don’t like to limit this to one type of visualization alone. In fact, it’s important to be familiar with a variety of data visualization options, so you can find the right approach to showcase your data to its fullest potential. A great way to use a variety of data visualization approaches in harmony is by making business dashboards. Dashboards are more robust but still comprehensive enough to showcase numerous data points in one image.

Dashboards

Dashboards might look complex on the surface but they’re actually one of the best ways to bring deep data insights together into one digestible format. That’s because it takes a variety of data points to tell a strong story. Dashboards enable you to quickly showcase several insights in one area, painting a clearer picture of what’s happening in the firm as a whole. Here’s a good example of what that looks like in action.

Data as a Service Data Visualization Dashboard

You can see a variety of metrics at play in this one dashboard. Leads are broken down by referral sources and locations. In a matter of seconds, you can see that your leads are increasing and know which channels are responsible for the increase, as well as which geographic regions are responding best to the messaging and sales teams on the ground. From there, you can continue to break it down by looking at your spending. Which channels are most expensive? Which bring you the most revenue?

This dashboard is just one example of the almost limitless possibilities available. Dashboards like this are a core component of DaaS deliverables because it’s one of the strongest ways of making the data accessible to everyone in your company regardless of department or expertise. By showcasing data in this way, you avoid falling victim to mistakes made by using guesswork. It becomes easy to digest, further enabling you to pull out actionable insights rather than making assumptions.

An Example in Action: Year-Over-Year vs. Convergence/Divergence Bands

To nail down the importance of the power of data visualization and why it’s such an important part of data looping and what Data as a Service providers deliver, let’s compare two types of visualizations.

If you’re like many companies, you’re probably charting basic analytics, such as your year-over-year (YoY) growth and compound annual growth rate (CAGR). Those charts might look a little something like this.

year over year sales

If you haven’t used these charts in your own presentations, you’ve probably seen something similar to this before. YoY Growth and CAGR are useful data points when you’re looking at your returns and profit margins. Charting these out in this way lets you see how the company has progressed over the years and where you’ve seen spikes in profits.

As valuable as this chart is, it’s lacking depth and detail specifically around spending. Some companies track sales alone, but then continue to allocate spending evenly across the board without taking seasonality, consumer trends, and more into consideration. This even spending could cost opportunities by continuing to put money toward channels and strategies that don’t give you a healthy enough return.

To find new growth opportunities and push those sales charts upward, you need to get deeper into your data. Visualizing your sales is important, but opportunities lie in the details.

In Buckley’s experience on Wall Street, he became adept at using many technical analysis tools to help him break down and forecast macro market momentum as well as micro-events. One such tool that became a staple in his arsenal was the use of Bollinger Bands.

 

bollinger bands

By Albert Callisto [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], from Wikimedia Commons 

Bollinger Bands are standard fare among stock traders as a momentum tool to understand overbought/oversold stock conditions, but since this may be the first time you’re seeing these monsters, here’s a quick rundown. Bollinger Bands are a technical analysis tool—specifically they are a type of trading band—used to create rigorous trading approaches, in particular for pattern recognition. Nothing is more important to forecasting than having a solid understanding in pattern recognition.

And as handy as these babies are to traders, their uses expand well past the financial sphere. For example, they’ve been used by the textile industry to detect anomalies in fabric patterns, and the International Civil Aviation Organization uses them to determine the effectiveness of global safety initiatives. Obviously, with such a variety of uses to their name, there’s more to Bollinger Bands than can be seen at a glance. That’s why we plan to dedicate one of our new posts to fully unraveling the secrets to how these can be applied to businesses, and fund allocation.

Part of pattern recognition is understanding how the bands converge (or come together) and how they diverge (or drift apart). Taking this concept and applying it to business, Buckley saw an opportunity to use the band idea to visually recognize patterns in operational and marketing spend against the backdrop of Year over Year (YoY) Return on Investment on that spend. And with that data in hand, the Convergence/Divergence Bands came into existence.  So again, Convergence/Divergence bands are a tool inspired by the Bollinger Bands and developed as a way to analyze and measure the efficacy of a company’s Spend to ROI health across each spending segment. In the graph below, we can drill down to how a company allocated its spend across its customer journey analytics.

Data as a Service convergence-divergence bands

When plotting out Convergence/Divergence Bands, like you see here, we strategically choose two metrics to pair alongside each other. In this example, we chose touchpoint spending on the Y-axis and time on the X-axis. The goal of this examination was to determine which year had the best return on investment based on the amount spent and how it sync’d across a pattern of divergence or convergence.

With these three lines plotted on top of each other, you can quickly see where the lines start to come together, or converge, and where they drift apart, or diverge. Convergence signals consistency; from one year to the next the spend in that season hasn’t needed shifting due to consistencies of demand/availability.  Areas of divergence showing inconsistency in spend are opportunities to look deeper into what caused that change, and what effects it had on ROI. From there we will do an analytical model to uncover where to let off the gas or where to accelerate spending.

This type of data visualization doesn’t have to be limited to hard financial metrics like spending either. To do this for our DaaS clients, we find metrics which showcase the experience a person has with a brand by pairing soft metrics—such as brand lift or consumer sentiment—alongside hard metrics, such as revenues and profits. There’s a lot of strategy that goes into which metrics to plot and how to compare the two, so although this might sound simple on the surface, it’s anything but. This approach is complex, so we’ll get into the details of how this is done in another post, but for the sake of this post, it’s important to note how we’re iterating on data visualization and using it to gather insights.

Data Mapping

As you close out the data loop and start visualizing the data points, it’s time to take the insights you gathered and map them out into action steps. This is called data mapping. It’s where a data as a service provider is able to help you turn your data investment into cash flow by mapping out opportunities available.

You’ve likely seen a data map before and not realized it. For example, you might map out your content marketing strategy to show where to create content, which touchpoints to focus on, how often to add new content, and more. Or, you can map your data to plan out your next Internet of Things (IoT) innovation or develop new products. This can be done for any type of project, but let’s bring it back to creating experience initiatives, which, we believe, is one of the best investments you can make with your data.

The data points that fascinate us the most are those that expose human behavior. This is the creativity aspect we addressed earlier when we talked about thinking like a startup. Getting creative and humanizing data enables you to see the person behind the charts, dashboards, and figures. As you take that humanized data, you can then map it out in a way that answers how to deliver an exceptional experience—the kind that will give you a healthy return on your investment.

One way we do that here at RocketSource is through our Customer Insight Map.

customer insight map

In our Customer Insight Map, we’ve taken the concept of mapping out how a typical customer moves through the buying process with your brand and then applied data to the map to tell a stronger story. Now, we’re only able to give you a small snippet of our Customer Insight Map in this post because it is a component of our upcoming workshop. If you’d like to see the full map in its entirety, you’re welcome to sign up, but for now, here’s the takeaway: data maps are a powerful way to incorporate data-driven insights into action steps that’ll help fuel your growth.

For example, in the Customer Insight Map here, we don’t exclusively look horizontally. Some of the best insights are gleaned when we look at this map vertically too. Taking this perspective, we’re able to break down each stage of the journey and get a better picture of where opportunities lie, which paths deserve the most investment, and more. All of this is done in an effort to turn that data into bigger profits and fuel business growth, and truly that’s where working with a DaaS provider becomes truly advantageous.

Believe it or not, accessibility to and democratization of data are just the tip of the iceberg when it comes to advantages businesses can get from working with effective DaaS providers. Contrary to popular belief, DaaS doesn’t only focus on setting up a new data collection system or Hadoop cluster. It goes far deeper than that by unifying data across departments, visualizing data to gain company-wide buy-in, inspiring growth and experience initiatives, and more.

Advantages of Data as a Service

Data as a Service has had major impacts on businesses and customers.

Jonathan Greene | CEO at RocketSource

Jonathan Greene | CEO at RocketSource

Our eyes just about popped out of our heads when we read this stat: For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Now those are real numbers that we can hang our hat on—and in fact, do hang our reputation on.

We’ve talked a lot about monetizing your data, but there are plenty of other benefits to working with a Data as a Service provider beyond getting bigger revenues. Lower costs, faster paths to innovative ventures, more agile decision making, data-driven cultures, and lowered risks are just a few of the successes we’ve seen when working with our clients.

Intelligent Initiatives Can Reduce Costs

Ever since the iPhone was released in 2007, the world as we know it looks very different. What started a decade ago as a device where you downloaded apps has now grown into a device that you turn to throughout the day to solve any number of problems. It’s become so ingrained in our society that Google has coined a term for them—Micro-moments.

Micro-moments are the times when Google says we, “reflexively turn to a device—increasingly a smartphone—to act on a need to learn something, do something, discover something, watch something, or buy something. They are intent-rich moments when decision are made and preferences shaped.”

Micro-moments are tremendous opportunities for brands today. They fuel what’s coming next in our data-centric world—predictive analytics.

As a company, you need to get to the future first, ahead of your customers, and be ready to greet them when they arrive. — Marc Benioff, Founder, Chairman, & CEO of salesforce.com

Predictive analytics delivers the personalized experience so many consumers are looking for today. Using micro-moment insights, you can better predict what a person needs to see from your business to keep progressing in their path-to-purchase. Until now, communications were left up to human guesswork to determine what various cohorts wanted. But predictive analytics are powerful because they don’t have to be left up to human interpretation and action. Algorithms are now able to analyze data points and pull out patterns to better predict future behaviors. This is called machine learning.

Deep learning goes a step further in the machine learning spectrum, beyond task-specific algorithms, and mimics human biological nervous systems. Based on the patterns learned, the machine is able to adjust its performance and output in order to respond to consumer preferences faster and easier than ever before. This adjustment is done without any human intervention on the backend, so analysis and reaction to those findings can happen 24 hours a day, 7 days a week, and 365 days a year.

A classic example of how machines learn is the Kalman filter. This filter works in a two-step process. The first step estimates what the human behavior will be. Then it works in real time taking in the incoming data, analyzing it using weighted averages, and adjusting based on the accumulated data. It looks for statistical noise over a given period of time and makes changes as needed.

These are all components that fall under the umbrella of artificial intelligence (AI). AI, in its most basic definition, are computer programs that operate like people. This doesn’t have to include machine learning or predictive analytics, but it often does. Companies are flocking to AI. In fact, Deloitte found that 76% of “aggressive adaptors” believe cognitive technologies, or technologies that deal with human behavior, such as thinking and perception, will substantially transform their product and service offerings in the next three years. 57% said these technologies will transform their companies internal processes in that same time frame.

AI was certainly a game changer for Orange Theory, a high-end gym. Growth in the fitness industry is no small feat. The fitness world is saturated with all sorts of competition from personal trainers, the popular CrossFit branded workouts, spin classes, and more. To break into this market requires creativity and, of course, data. Orange Theory used both to gain massive ground.

In December 2017, Orangetheory deployed an AI platform that enabled the chain to reduce their cost per lead from $20 to $8. The platform they used to achieve this growth was designed to study and analyze the sweet spot for where to gain new leads.

Prior to using AI, they were targeting their spending to women around the age of 35 with an average income of $80,000 – $100,000. However, through their AI platform, they were able to discover a massive following lurking in the shadows of both women and men ages 18 – 25. Deeper than that, they also uncovered that their audience came from sports (especially college sports) and music platforms.

Using these insights, Orangetheory put together a new media campaign called “More Orangetheory, More Life.” Kevin Keith, the chief brand officer at Orangetheory, said: “With AI, we know the behavior of, say, a 27-year old female in Chicago enough to deliver her a message that will resonate with her more deeply.”

Orange Theory Fitness

The message spoke directly toward the sports and music-loving audience they found they were attracting. As a result, Orange Theory grew dramatically in only a few years. It opened almost 1,000 studios in 15 countries and—at the time of writing this—has about 623,000 members.

This is what it looks like to run an intelligent initiative. Orangetheory didn’t guess what their audience wanted. They used insights gleaned from data to pull out messages that trigger emotional responses and human behavior. But this type of emotional appeal didn’t stop with their advertising. Once in a studio, the fitness giant uses a person’s individual data to personalize the experience.

Execute on New Paths Faster

When you run an intelligent operation and use data to make insights-centric decisions, you’re able to move with momentum. By taking out the guesswork of what consumers want, you’re able to iterate and innovate faster than before, while mitigating risk. Data as a Service providers help create systems to extract that data and use it to personalize experiences quickly, methodically, and strategically.

Take a look at Chicisimo’s machine learning approach as a good example. This app helps its users decide what everyday outfit is right for them based on personal preferences. As you can imagine the results are quite subjective, so in order to accomplish its goal, the founders knew that they needed to personalize the experience. They turned to consumer data to tailor how the person used the app.

The app didn’t have a problem getting users to sign up and try it out, so they turned their focus to a bigger goal—retaining their users once they were on board. After some strategizing, they looked to their data as a roadmap for where and how to improve their customer retention.

data-as-a-service-chicismo

The team knew that in order to improve retention they needed to get users engaged in the app within the first few seconds. To do this, Chicisimo looked to their data from their current loyal users to find out which behaviors drove the most and longest-lasting engagement. Using these insights, they iterated on the initial sign up process. The goal here was to better retain new sign ups by having them walk through the steps that were most likely to get them to become loyal users of the app within the first seven minutes of signing up.

Then, they created behavioral cohorts. By breaking up users into various cohorts, they were able to tweak what they first saw when they logged into the app and more effectively align user behavior with what they saw when they logged in each time.

Finally, they iterated on how the app learned their user’s preferences over time. By developing data loops to gather and understand buyers behavior, they were better able to close those loops and map out the best paths to act on without guesswork or high risk. They made improvements to the app faster than many of their competitors and —as a result of their fast action—they grew quickly. By harnessing the user data and behavioral patterns, the app was able to scale from zero users to over 4-million users in three years. They got featured as the App of the Day throughout the world all because of their innovative approach to solving the common human dilemma of not knowing what to wear.

Finding these insights takes skill, infrastructure, and expertise—three things that many organizations don’t have, as you might recall from the Retail Systems Research study we showed you above. A DaaS provider gets you started by setting up the systems to gather the right data, and then takes that data and showcases it in a way that enables you to quickly develop new experience initiatives like this Chicisimo did to spur growth in their app.

Unbiased Insights

One mistake we’ve seen, especially when it comes to running an agile operation, is making decisions based on bias. A BI-Survey recently found that 58% of the people said they don’t make data-driven decisions in business, but instead base at least half of their regular decisions on gut feel or experience. Whether we like to admit it or not, we, as humans, are biased by nature, so making decisions based on our own internal guesswork is problematic.

We bring this bias up because it’s costing businesses a tremendous amount of money. When a company uses guesswork to determine what the market demands, their biases tend to steer the company’s ship instead of data, creating an expensive disconnect. Breaking away from this bias and aligning your brand with your customer’s desired experience is one of the best ways to grow. That’s what we call StoryVesting. Here’s what it looks like.

daas-storyvesting-framework

We’re not going to dig into all of the intricacies of StoryVesting on this post because you’d be reading for days. The basic premise, and what you need to know for the sake of this post, is the importance of strategic, data-driven alignment, which is hard to achieve when you’re focused on gut reactions from internal team members.

By aligning your company culture with both consumer and employee wants and needs, you’re in a prime position to build out effective customer experience (CX) or employee experience (EX) initiatives. Hiring a third-party DaaS provider can help shed light on the internal and external demands using data-centric insights and an outsider’s unbiased assessment.

Take Walmart as an example of how this can come to fruition. As Hurricane Frances neared Florida in 2004, Walmart chose data-driven decision making when choosing which products to stock their shelves with prior to the storm’s arrival. After scanning a terabyte of customer history, they determined that it wasn’t water and toilet paper that customers wanted. It was strawberry Pop-Tarts and beer. So, they filled their delivery trucks with toaster pastries and frosty beverages and drove them to where Floridians were hunkering down. The people of Florida were happy and so was Walmart, who spun a profit off their ability to use data to align with customer demands.

DaaS providers help you find areas where you can grow by taking a methodical, strategic approach in gathering, mining, and analyzing data in a way that delivers unbiased insights into what customers really want at their core. Outsourcing your data and analytics can, in turn, help bring more alignment to your customer experiences.

Breaking Down Silo Walls

Earlier we talked about the importance of closing the data loop with data visualization and this is why. When you don’t close up the loop, you start to close something else—the cross-department communication. Silo walls can be toxic to organizations. When teams don’t collaborate and communicate effectively, they’re less able to effectively tap into the insights data provides them.

Democratizing the data is one of the best ways to close out the data loop and distribute that information across the organization. It’s also one of the most difficult things to do. Part of making data accessible is making it relevant to everyone’s needs. Each department has unique goals, which means they have unique data points to monitor and unique insights they need when making their decisions. A DaaS provider is skilled in pulling out the most applicable data points and those that answer the customer’s biggest concerns. Then, they take those data points and bring them organization-wide to ensure everyone’s speaking the same language and tapping into the same insights when steering their department’s ship.

This concept of democratizing data makes an impact organization wide by giving teams the tools they need to do their jobs exceptionally well. Look at this recent McKinsey study on fueling growth through monetization and what can happen as you start to infusing these insights into business practices.

Data as a Service Effect on Business Practices

Here, you can see that data and analytics have significantly or fundamentally impacted a variety of departments, in a variety of industries. This couldn’t have happened if the data remained in the hands of the data scientists alone. It had to be presented in a way that spoke to each of these departments, as well as to the organization as a whole, in order for significant changes to happen.

But democratizing data is only the first step. Next, you have to get teams speaking the same language to each other about that data. Data initiatives are such a massive investment and so complicated that a lot of executives, including CIOs, CXOs, CTOs, CMOs and Chief Data Scientists, get lost because they don’t know how to make it actionable to the customer nor the employee. For example, the CMO doesn’t have a solid grasp on how to mine or model data to gather insights about real-time channel sentiment shifts, so they hand the data analysis off to the CIO who really doesn’t understand how to apply the analysis to well-defined ROI model based upon the qualitative data coming in. Another example is when the CXO doesn’t understand how to develop an accurate data mapping and looping process, so they hand it over to the CDS who never gets an accurate view of their employee’s experience because of what we call, silo’ing. Suddenly, silo walls become harder than ever to tear down because no one’s speaking the same language about data and the processes needed to glean insights from the information available. The data ends up remaining dispersed and lacking cohesion across departments.

The process of pulling out insights becomes smoother when executives ride in sync with each other. To do that, everyone on the C-Suite must have a basic understanding of data and the role it plays in achieving the business’s goals. That means that the entire C-Suite must have a solid grasp of the type of insights needed and how they’re gleaned. To break down silo walls, it’s critical that everyone gets skilled up and in sync with each other so that they can start to talk the talk and communicate better across the board.

We’re strong believers in the value of skilling teams up. It was that belief paired with the fact that few executives and key leaders put aside the time to learn the intricacies of data wrangling that prompted us to create our Level Next Workshop—a workshop designed specifically to help busy forward-thinkers advance their skill set in intelligent CX data looping and data mapping.

level next workshop

Stay tuned for updates regarding the release of a powerful workshop, facilitated by us, a DaaS provider, guiding you on how to bring teams together over data and analytics and how to use it to fuel your CX initiatives.

The Challenges of Outsourcing Data Services

We offer data as a service, and yet we’re not shy about mentioning both the upsides and downsides of outsourcing your data and analytics. That’s because, when it comes to something as critical to your business as data, it’s important that you have a clear idea of what happens when you hand over the keys to this information.

Outsourcing to a DaaS provider is not something that should be done blindly, but rather with a healthy amount of research, consideration, and understanding. Without doing your due diligence, you could run into serious problems as a result of dirty data, misinterpretation of complex datasets, and data security issues.

Data Set Hygiene

One large nuance that you have to consider when you’re hiring a DaaS vendor, is syncing your data across the board. The DaaS provider needs to be able to truly understand how to cleanly sync with other data sets and nodes. This clean sync across the board is what we refer to as data hygiene.

We’ve worked with several Fortune 50 companies, and in our experience, we’ve learned that large corporations have a slew of vendors they’re working with. The problems arise as they bring outside data prepared by third-party vendors back to their organization and go through their own set of scientific rules and cleaning procedures. They’re now mashing internal data with the provider’s data sets without knowing if both are comparable or combinable.

Another concern is whether or not the vendor’s data up to your standards in terms of how it’s handled. If you’re a corporate behemoth, you’ll know that your internal data is clean, but how do you know that outside vendor’s data is just as clean? Even if it is technically clean, how do you know it’s data that can blend with your own?

For example, we recently conducted a survey for one of our clients. During this survey we asked the same exact questions in a variety of locations:

  • In the parking lot
  • In the entryway to the store
  • In the back of the store
  • At a competitor’s store

Even though the questions were identical word-for-word, we were unable to combine these datasets because the customer’s answers would differ depending on the timing and location they were asked. If we had combined them, the data would be dirty and unreliable. Instead, we compared and contrasted the datasets but never merged them to form one large data lake in order to maintain the cleanliness of the data.

Data hygiene is a real struggle and because of it, some companies won’t sign off on initiatives with a DaaS provider until they take the data through their own internal system to ensure it’s handled in the way they deem appropriate. That becomes an issue as you work with a DaaS provider because they need deep access into your database to do their jobs effectively. Sometimes, the differences in the data sets means the figures could be skewed. For example, if a vendor shows you that you’re getting a 30% lift and your DaaS provider only shows a 5% lift, there’s a problem.

Data science is an animal. There are so many operational nuances that must be considered. If you and your DaaS provider don’t know how to traverse those nuances and govern the data delivery project, then you’re going to spend millions of dollars with the wrong vendor without getting to the insights you need to make things actionable and valuable.

Navigating the Complex

One of the biggest challenges to data as a service is that data itself is so complex by nature. The reason why DaaS hasn’t taken off yet is because many employees in large corporations, and even some smaller vendors, don’t know how to navigate various datasets very well. That’s because they’re hiring people who are solely focused on data science, or who are just fresh out of school and haven’t gotten their hands dirty enough yet with analytics. For example, many young data scientists look exclusively at one set of information, but an experienced DaaS provider understands that more impactful insights can be gleaned by comparing and contrasting data. Here’s an example of how we compare and contrast paths-to-purchase.

Funnel and Journey Innovation

Here, you can see two different, yet equally complex paths. By breaking each of them down, we’re better able to navigate complexities to find profitable solutions that deliver healthy ROI. The challenge here is making sure you know you’re hiring the right kind of provider who can navigate complex approaches, like this one, before you bring them on board.

High level consultancies and deep data solutions providers know how to harness data well—usually better than the corporations they’re serving—which is why having these providers on board who can navigate the complexities is so valuable. They’re not only familiar with the operational nuances we addressed above, but they also know how to use data to tell the right story and gain buy-in, which is critical for any organization running an agile management cycle.

Data as a Service may answer the question “Is our investment in data and analytics paying off?” — Buckley Barlow

Data as a Service takes methodical, strategic thinking. It’s strategic because your data must answer the overall strategies of the company. It’s methodical because it’s nuanced and has to drive to a certain objective. At a certain point, there’s a question that’s constantly being asked by the organization and DaaS providers need to be able to answer quickly and clearly. That question is, “is the analysis working?”

For example, if you build a $2 billion Hadoop cluster, you want to know if it was worth it to mine unstructured data from the social sphere and what employees are saying internally. You want to know that you’re seeing the return on that hefty investment. The right DaaS providers will be able to show you the ROI. Choosing your partners wisely will ensure that you effectively navigate the complexities associated with data science and that you can see it pay off in actual revenues.

Data Security

Anytime data is transmitted there’s a threat for it to be hacked. Because of the sensitive content within big data, for organizations of all sizes, an attack from an outside cyber criminal isn’t a small blip in the DaaS radar. It’s something to take very seriously.

Often, the data that’s getting collected, mined, and turned into actionable insights is transactional data, which means it includes private customer info and financials—none of which your business, or your customers, can afford to have leaked. These insights must stay internal if you want to innovate before the competition. Data leaks not only cost you serious money and they hurt your brand image, but also your competitive edge—and that’s just the tip of the iceberg. Take a look at this slide from the 2018 Mary Meeker presentation embedded above.

data security

Threats are getting increasingly sophisticated, which means the need for cybersecurity and data security is more important than ever before. Although the sophistication of the risks are increasing, so is the sophistication with which to protect your data. It’s important to be aware of the risks out there but they should not stop you from moving to a DaaS provider. The right DaaS provider will put your data security above all else while keeping you compliant with data governance, such as GDPR. The key here is to be discerning about who you give the keys to when choosing a DaaS provider.

By proceeding with caution and doing your due diligence, you can help mitigate avoidable data security risks. Bob Violio confirms this point and mentions the importance of doing your due diligence in his post, “The dirty dozen: 12 top cloud security threats for 2018.”

Before you settle in on a DaaS provider, talk to them about their security measures. You must vet these providers and understand how they’ll use the data you give them access to. This requires some research on your end, but that research will pay off in dividends as you move forward. Every reputable provider will welcome your questions and respect your research.

Adding a Team to Your Internal Structure

When you’re ready to take that next step and outsource to a data as a service provider, you’ll need to go through an implementation stage. This is the time when the third party team integrates into your internal structure. This is a big next step and one that should be done methodically. Here’s a roadmap of how we suggest approaching this new relationship.

Start Small

As eager as you are to get a better grasp on consumer behavior, demands, and the direction of the market, this isn’t a relationship to jump into head first. You’re better off starting small.

In the beginning, give your new DaaS provider things they can’t break. Stay away from handing over anything with code. Code is complex and if something happens to it, you could spend time fixing the error instead of finding new insights. It’s equally important that you stay away from handing over the keys to any database architecture. This is another critical piece of your business and if it breaks, you must fix it immediately.

Start with a small project, such as data visualization.

Solutions & Planning

Yes, you went through the process of vetting and getting to know your DaaS provider before hiring them, but now it’s time to see first-hand what the company can do for you. Data visualization, in particular, gives the provider a chance to showcase its data chops. It lets them prove their expertise with datasets, such as goal and event tracking in Google Analytics, or Adobe Analytics tagging.

Remember, just because a provider looks good on paper doesn’t mean they’re the right fit for your company. Starting with small projects will let you two work out any kinks and solidify that this is a provider you can trust with your valuable data.

Progress Slowly and Front Facing

As you start to get to know the provider, progress slowly with the level and amount of projects you give. If you started with data visualization, progress to conversion rate optimization (CRO). Give your DaaS provider access to your funnel and let them work their magic optimizing it.

Remember, the DaaS provider should be able to take your current analytics and dive deep with analysis. From that analysis, they should be able to give you actionable insights that’ll help you gain more market share. Let them prove that they can optimize critical conversion funnels by using data to demonstrate their expertise and that they’re worth the initial investment.

If all is going well, keep progressing. Turn up the volume of work for full CRO. To truly optimize for conversions, you need to understand the CX journey frontwards and backwards, side to side. This shouldn’t be done with guesswork or surface level assumptions. If you use the right people and combine the right tools, you can get clean, easy-to-interpret data.

Back Office Jobs

Until now we’ve only covered front facing or customer facing events and services. A superior DaaS provider will also provide back office services. Back office services from a DaaS provider are much more complex. That’s why this component should be the final thing you hire a provider to manage.

Effective DaaS providers worthy of your investment aren’t solely focused on systems or feeding more big data into your organization. Ultimately, you want your DaaS provider to work arm in arm with your IT department, your business intelligence team, and your analytics teams to pull out the most impactful insights.

This relationship requires a tremendous amount of collaboration over a variety of tech tools and platforms. It’s complex, yes, but when done right it’s incredibly powerful.

Large companies often work with businesses like SAP, Oracle, and Microsoft to create a seamless way to use technology to gain transformative insights. Mid-tier or legacy companies tend to work with Mulesoft or other providers to integrate this level of data-driven insight into their business. Smaller companies tend to work more with web development agencies and consulting shops to dive into the data details. Your DaaS provider should be familiar with the tool, or tools, you use.

The items you handle in the back office is a massive competitive differentiator, which is why it’s absolutely critical that they are only handled by a vetted DaaS provider.

Who Should Outsource Their Data and Analytics?

No matter the size of your business, you need data and analytics—no ifs, ands, or buts about it. What differs between small, medium and large businesses isn’t the need for data and analytics—it’s how the data is gathered, mined, and put to use. Every business travels up this S-curve of business growth and at every stage requires solid information from the market to understand the best next step to take.

Daas S curve of business

Regardless of the size of your business, the S-curve of business can signal to your company when it’s time to pivot and when it makes sense to bring a third-party on board. This is certainly the case for knowing when it’s time to scale up your data and analytics efforts with a DaaS provider. Here’s a breakdown of how your company, based on size, can use a DaaS provider to aid in those efforts.

Small Businesses

Typically a small business will gather data from one platform, such as Google Analytics. From there, the team gathering the data will take it to the CEO, founder, or CFO to try to analyze and use to find the next steps. So far so good. But what happens next stifles the growth of many small businesses. It’s at that point that when a data as a service (DaaS) provider should enter the picture.

A DaaS provider can:

  • Minimize the risk of a small business entering analysis paralysis;
  • Pull out deeper insights than what’s available via Google Analytics;
  • Propel them out of an innovation loop and to the next level of growth.

Small businesses fall into an all-too-common trap of getting stuck in the mud while filtering all the insights that pour into the firm. A DaaS provider will pull out the most actionable insights and then puts those into a data visualization tool to make it easy for everyone in the company to see where the biggest opportunities lie.

Mid-Tier Companies

As a business grows, they start bringing on full-time staff. They also start bringing on new platforms to juggle all of the data available. It’s at this point that the need for more data, and better data, grows.

Mid-tier businesses are poised to be disruptors in the market. These businesses are at an exciting crux in their development. Innovate fast enough and they’ll propel their brand forward on the S-curve of business growth. Stagnate because of a lack of using the right data and the competition will blow past them.

The data gathered from the more advanced platforms stops being so quantitative (which is easier to understand). Instead, mid-tier businesses need qualitative data to understand the human behavior taking place throughout the customer’s journey with the brand. For example, mid-tier companies often use heat maps to understand human behavior on their website. They use usability studies to find ways to innovate product lines. Or, these businesses use NPS scores to gauge sticky points in the customer loyalty.

It’s at this point—when the data is turning away from simple quantitative insights to bring in more qualitative data—that having a third-party DaaS provider immersed with the data is critical. A DaaS provider helps mid-tier companies:

  • Dig deeper to gain qualitative insights in addition to quantitative;
  • Deploy strategic methodology for gathering information;
  • Set up the infrastructure needed to filter the overload of information pouring in.

DaaS makes it easier to correlate insights from numbers and with human behavior—something that, without expert knowledge, is incredibly difficult to do right—especially when you consider cultural biases entering the mix.

By the time a company hits mid-tier status, they have a solid culture ingrained in their employees. Employees naturally adopt a lens with which they view the data, making it harder to analyze the information filtering into the firm correctly. A DaaS provider is instrumental in bridging that gap and offering an unbiased analysis of the market’s voice.

Large Corporations

By the time a company emerges from the mid-tier level and becomes a strong contender in their niche, they’re using powerful data platforms to gather insights. These platforms are a combination of the ones small and mid-tier businesses use but they also have an added component. This is the stage when Hadoop clusters get built and data scientists get hired just to manage all of the information flowing into the firm.

Although there’s a team of data scientists sifting through and visualizing the data pouring in, a DaaS provider is still needed to help steer the data ship in the following ways:

  • Structure the overload of unstructured data;
  • Tap into data lakes and use the information available to map out actionable insights;
  • Democratize complex datasets so they can be used organization-wide and break down silo walls.

Large companies are often plagued by operating out of silos. Instead of talking across departments, C-Suite executives focus on leading from the top down. It’s rare for a CMO to collaborate regularly with a CIO and COO. It’s this culture of silos that leads to a lack of process optimization.

A DaaS provider is equipped to and expert at breaking down those silos and optimizing team members. Data service providers that are able to keep every team working cohesively toward their North Star metric. In turn, by keeping a large organizational ship pointed in the right direction—toward growth—teams are better able to come together and pull out new ideas based on market feedback to implement with minimal risk. Working purely internally means running the risk of operating out of silos and thus, never innovating.

Wrapping Up

Anyone worth their salt knows the importance of using data to run an intelligent, agile operation. We’ve talked a lot about the many reasons why, so by now, you might be wondering if we are one of the “right” data as a service providers that we kept referring to above. To that end, we challenge you to find out for yourselves, and challenge us in turn. Hold us to your necessarily high standards—we’ll never balk, because it’s what we would do in your shoes. In fact, challenge us with no risk to yourself with a discovery assessment.

The first step in working with RocketSource is always a discovery assessment. This assessment is critical in determining the best starting point for engagement. That’s because every company is different. Every business we work with is at a different stage in their data journey. The first step for us is to find out that stage and where we can best fit in to help simplify the complex.

If you’re ready to use this for your company, so you can separate your business from the competition, we’d like to be your first call.

Want a few examples of companies we’ve worked with in the past? We can’t give it to you (and boy, we wish we could). That’s because we’ve received the clearance to work deep into the code and database structures of some of the largest brands in the world. We never advertise or promote the specifics of the work we’ve done for our client list simply due to the fact that many are publicly traded companies. We’re a secret weapon to their advanced teams. Our experts know how to hang with the best-in-class technologists and data architects, and that’s just what we do day in and day out.

Where does all of this leave you? Hopefully, by now your feet feel a little more firmly planted on the ground when it comes to understanding DaaS. It’s a critical component in today’s data-rich world and we want you to have a firm grasp on it.

Ready to explore how your business can benefit from Data as a Service and whether RocketSource is the right provider to move you closer to your goals? We’d love to talk. 

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Written by Buckley Barlow & Jonathan Greene

Buckley Barlow

Founding partner of RocketSource. Author of The Growth Code and StoryVesting. I live at the digital intersection of strategy, tech, creative and data. I roll up my sleeves with some of the most forward-thinking teams in the world to help them deliver best-in-class brand outcomes.

Jonathan Greene

Jonathan is a strategic technologist and has been designing and deploying custom technology and digital solutions for almost 20 years. His ability to successfully execute on a technology strategy is underscored by his skill in anticipating and managing the tech stack, the processes requirements and the resources needed to deliver substantial returns on investment. He has a unique ability to understand complex projects and business models and has an astute eye for navigating challenging transformation and growth assignments.

Downloads and Resources

Data as a Service Header Image

Data as a Service

Convergence/Divergence Bands

daas-convergence/divergence-bands

Challenges of Data Analytics

data as a service challenges

The Benefits of Data as a Service

Roles Within Data as a Service

Data Visualization Dashboard

Personalized Experiences

The StoryVesting Framework

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