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.
What Happens in the Data as a Service Process?
Data as a Service involves data looping, data visualization and data mapping. Each component is critical for helping you reach a bigger goal.
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.
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.
The thought of Data as a Service (DaaS) often conjures up images of complex algorithms and machine learning, but at RocketSource we think of making data sets accurate, easy to digest and actionable. That’s not an easy task. Our years of experience as a digital consulting firm have shown us that executives continually struggle to harness the firehose of information pouring into their businesses. 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 who 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 is 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 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 together in an effort to apply our collective skill sets. Our goal is to give you as much insight into 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 audio soundbite in the post to hear us personally dive deeper into 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 in 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 versus future state, we leave it to Gartner and other leading research firms to speculate on how the latest and greatest technologies 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 worth adopting 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 been little adoption in the marketplace, so 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 through quickly 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.
You can see that Data as a Service is on the rise but Gartner deems that it’s still 5-10 years from the Plateau of Productivity, 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 upward slope of positive media hype. It’s still early enough 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 breakdown 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?
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 — perhaps 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 in both in SaaS and in the “as a service” family in general. A relatively recent and notable addition to these offerings is a cloud-based strategy known as Data as a Service (DaaS).
Data as a Service wrangles the overload of information out there today and makes it available across all departments, anytime and 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 to them. The more data you have 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 reason wasn’t solely that they didn’t have the expertise (although that was the case for 46% of respondents). 56% of respondents said it was because they didn’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 analytical hurdles. They’re hurdles in operations and infrastructure and are surprisingly common among companies striving to collect and crunch data independently.
As you can see from the findings showcased in this image, the lack of infrastructure is enough to prevent over half of the surveyed companies from running an intelligent operation. If your organization isn’t 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 data is available and how to tap into it. In the study above, you can see that 37% of companies didn’t know where to begin gathering 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, online content, handwritten feedback and other, similar elements. Sound like your 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 that 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 maintaining 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 its clients.
As you can see, this service goes beyond building algorithms to fill your data lakes. Data science requires you to 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 putting 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, those small groups of people provide motivation for the startup to create a product based on a gut feel for what 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 that 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, as generally happens 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 gather enough information to validate assumptions about 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 draw out insights from the collected data 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 turning hard data into actual revenue.
Data wrangling, data tuning, data mining and data lakes are common buzzphrases, but they’re only a portion of the Data as a Service offering. By focusing exclusively on building strategies around those phrases, 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 you can realize 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
Generating revenues from data doesn’t mean selling datasets for cash — data monetization goes far deeper than that. It’s about collecting and packaging data insights in a way that delivers 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 here at RocketSource is second to none because we go after root-cause analysis to uncover the cognitive associations of your customer’s motivations and behavior and understand how those associations match 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 consumers’ overarching purchase behavior.
Experience has taught us that many business challenges can be analyzed and optimized by gaining a wider perspective. Through this wider perspective we can address 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 and better showcase the values achieved. We can then correlate them with the key ROI drivers for our client.
Bringing in data to your organization is only a small part of what it takes to fuel growth and drive more cash flow into your business. In order to see a return from the thousands or millions of dollars spent setting up these robust systems, you must have a plan in place to use the data you collect from your customers to derive better experiences with your brand. These better experiences are the ultimate point of arrival because they’ll set your business apart from the competition and get you closer to seeing hockey stick-style sales charts.
Improving customer experience initiatives requires that your business has layers that will make your company more intelligent. One of those layers is data analytics.
Data analytics has been shown to strengthen customer experiences in both client-facing businesses and 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.
The data here shows that one of the benefits of Data as a Service is improved customer experiences. When monetizing data fronts it’s important to find ways to increase various metrics, not only to build customer experiences, but to build the business as a whole. Onboarding, churn rates, time-on-site and scroll depth are all examples of critical engagement metrics. The more engagement you have, the more involved consumers feel and the more they are driven to make repeat purchases from your brand. That engagement signals something bigger — positive customer experiences.
According to Accenture, more and more consumers are turning off personal data taps, making it harder for companies to gather the data needed to improve experiences. Accenture’s data shows that 44% of US consumers are frustrated by companies’ lack of personalized service. Still, 49% of consumers said they were concerned with personal data privacy, leading them to opt out of personal data taps. This means that, to continue tapping into data to drive experience initiatives and grow sales, companies must come up with creative ways to collect data while simultaneously mastering how they use that data 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 data monetization. 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 monitor the pulse of the market. If you’re like us and many other forward-thinkers, you probably regularly read reports like Mary Meeker’s 2018 Internet Trends report. In the most recently released version, Meeker’s data findings back up our conviction that customer experience is paramount. 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 to return to later. In the meantime, here are some of our favorite takeaways pertaining to data usage, personalization and the customer’s experience:
- Search terms with “near me” have grown 900%, which showcases 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.
- Deloitte’s study also showed that 79% of American consumers are willing to share personal data when it involves a “clear personal benefit.”
- Slide 79 presents one notable business that’s accomplishing the goal of getting consumers to share data, and then using that data to deliver a clear personal benefit — 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 customer’s taste preferences and individual needs. This personalization isn’t done through guesswork or purely through human analysis. The Stitch Fix model is driven by 85 data points that customers willingly provide, such as style, size, fit, and price preference. Stitch Fix also collects offbeat details, such as areas of the body where clothes typically fit awkwardly or areas the customer 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 to select the pieces to include in the customer’s bo. The end result is a data-driven experience that feels personal and reminiscent of the days when store tailors greeted you by name and took individual measurements.
Mary Meeker is clearly as fascinated by Stitch Fix’s data-driven strategy as we are because she included the on-demand clothing company in her report two years in a row. Her 2017 Internet Trends Report showed how Stitch Fix pairs data science with personal fashion preferences by highlighting the clothing empire’s computer generated designs that are based on customer feedback, product attributes and data science.
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 with modern shoppers, who want on-demand, personalized services. Although many 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.
Stitch Fix’s success is just one example of the importance of having the full package — data and personalized service — in place. Personalization has proven paramount in the otherwise impersonal ecosystem of online shopping. Looking at McKinsey’s 2018 state of the subscription economy, you can see why.
As you can see from these radar graphs, personal recommendation was one of the top three reasons for people to initiate a subscription. Once subscribed, personalized experience was the top consideration when deciding whether to continue a subscription. It’s up to subscription services to deliver an exceptional personalized experience to keep people coming back and get them to recommend the service to others.
This expectation for personalization isn’t exclusive to subscription models. Customers today demand personalized interaction both online and 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 personalized experiences that keep people engaged and buying time and again.
These types of insights uncovered by DaaS providers 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 is a systematic way to collect information, pull out insights and 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 go too far here, we want to address something that tends to weigh heavy on people’s minds — the fear that using technology to gather data and draw insights will result in lost jobs. 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 can do in half the time. What a world. People worried that the backhoe and other industrial machines would eliminate their jobs. Were machines taking over the world? Not quite. Unemployment rates stayed relatively low as those people were reassigned to other areas that needed a human touch. Calculations suggest that during the Industrial Revolution the unemployment rate topped out at 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 society as a whole.
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 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 wrote an article about how automation makes us rethink jobs. In the article they say that, “Deconstructing and then reconfiguring the components within jobs reveals human-automation combinations that are more efficient, effective, and impactful.” Although it may seem as if machines are threatening to take over our work, they’re really just opening up new opportunities for jobs that still require human thought. We’re a long time from seeing a replacement for human empathy and the need for human intervention, meaning 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 through which we collect data. Data looping requires human expertise paired with the power of automation to give us the deep, rich insights needed to 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, which requires the skill and expertise of a variety of people in a variety of roles.
Here you can see eight roles in the data looping process, each of which require collaboration across a variety of departments. The process starts by designing and crafting content for the methods you’ll use to 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 jarring experience while you gather information. The person or persons 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 responses toward what the company wants to hear.
Once you’re ready to deploy your data collection method, 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 will typically be involved to help aggregate all of that data from the various sources.
As you move into the mining and modeling phase, 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 in building out your journey analytics requires a system for gathering both qualitative and quantitative data. But gathering data alone isn’t enough. For example, if you’ve built a Hadoop to centralize disparate databases, that infrastructure 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 data and not closing the data loop, you’re in trouble. Silo walls go up, and soon departments stop communicating with each other 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 that can be avoided by visualizing your data and relating them to your business’s why. Until your data tells a complete and relevant story, you’ll struggle to extract strong insights and monetize it. In the last step of data looping, that handing these insights back to a skilled designer will enable you to bring these findings to the rest of your organization and break down those silo walls.
The final step in the data looping process is data visualization. In this step, 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. This involves making the insights understandable across all departments so that 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 through business dashboards. Dashboards are more robust but still comprehensive enough to showcase numerous data points in one image.
It takes a variety of data points to tell a strong story. 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. 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
There are 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 is just one example of the almost limitless possibilities that dashboards offer. Dashboards like this one are a core component of DaaS deliverables because they’re one of the strongest ways of making 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. Data becomes easy to digest, which further enables 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 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). These charts might look a little something like this:
If you haven’t used these charts in your own presentations, you’ve probably at least seen something similar. YoY Growth and CAGR are useful data points when you’re looking at returns and profit margins. Charting these out in this way lets you see how the company has progressed over the years, including where you’ve seen spikes in profits.
As valuable as this chart is, it lacks 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 other factors into consideration. Allocating spending evenly in this way could cost opportunities by continuing to put money toward channels and strategies that don’t give a healthy enough return.
To find new growth opportunities and push your 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 are standard fare among stock traders, used as a momentum tool to understand overbought/oversold stock conditions. As this may be the first time you’re seeing these monsters, here’s a quick rundown. Bollinger Bands are a technical analysis tool — specifically, a type of trading band — used to create rigorous trading approaches, particularly for pattern recognition. Nothing is more important to forecasting than having a solid understanding of pattern recognition.
And as handy as these babies are for traders, their uses extend well beyond 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, 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 they can be applied to businesses and to fund allocation.
Part of pattern recognition is understanding how the bands converge (come together) and how they diverge (drift apart). In applying this concept to business, Buckley saw an opportunity to visually recognize patterns in operational and marketing spend against the backdrop of Year-over-Year (YoY) Return on Investment on that spend. Armed with this information, he created Convergence/Divergence Bands.
Inspired by Bollinger Bands, Convergence/Divergence bands offer a way to analyze and measure the efficacy of a company’s Spend to ROI health across each spending segment. The graph below shows how we can drill down to see how a company allocated its spend across its customer journey analytics.
When plotting out Convergence/Divergence Bands, we strategically choose two metrics to pair side by side. In the example above, 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 how the amount spent synced across a pattern of divergence or convergence.
With these three lines plotted on top of each other, you can quickly see where they 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 didn’t needed shifting due to consistencies of demand/availability. Areas of divergence show inconsistency in spend, and are opportunities to look deeper into what caused the change and what effects it had on ROI. From there we do an analytical model to uncover where to slow down or accelerate spending.
This type of data visualization doesn’t have to be limited to hard financial metrics like spending. To do this for our DaaS clients, we find metrics that showcase the experience a person has with a brand by pairing soft metrics, such as brand lift or consumer sentiment, with hard metrics, such as revenues and profits. There’s a lot of strategy that goes into which metrics to plot and how to compare them. This approach is complex, so we’ll get into the details in another post, but for now it’s important to note how we’re iterating on data visualization and using it to gather insights.
As you close out the data loop and start visualizing the data points, it’s time to map the insights you gathered 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 available opportunities.
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 may 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 people behind the charts, dashboards and figures. You can map that humanized data in a way that tells you how to deliver an exceptional experience that will give you a healthy return on your investment.
One way we do that here at RocketSource is through our Customer Insight Map (CIM).
Our CIM maps out a typical customer’s journey through the buying process with your brand and then adds data to the map to tell a stronger story. We’re only able to give you a small snippet of our Customer Insight Map in this post because it’s a component of our upcoming workshop. If you’d like to see the map in its entirety you’re welcome to sign up, but here’s the takeaway for now: 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, which is how 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 the advantages of working with effective DaaS providers. Contrary to popular belief, DaaS doesn’t just focus on setting up new data collection systems or Hadoop clusters. 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
Our eyes nearly 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. Those are real numbers that we can hang our hat on — and do, in fact, hang our reputation on.
We’ve talked a lot about monetizing 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 our clients have experienced through working with us.
Intelligent Initiatives Can Reduce Costs
The world changed when the first iPhone was released in 2007. What started a decade ago as a way to download and use apps has grown into a powerful device that millions turn to throughout the day to solve any number of problems. Its use has become so ingrained in our society that Google has coined a term for these times we access our phones — micro-moments.
During micro-moments, according to Google, 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 decisions 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, determining what various cohorts wanted required human guesswork. Predictive analytics, on the other hand, doesn’t involve human interpretation and action, and is therefore much more powerful than guesswork. 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 along the machine learning spectrum, beyond task-specific algorithms, and mimics human biological nervous systems. Based on learned patterns, the machine is able to adjust its performance and output 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, 365 days a year.
The Kalman filter is a classic example of how machines learn. This filter is a two-step process. The first step estimates what the human behavior will be. During the second step, the machine works in real time to collect incoming data, analyze it using weighted averages and make adjustments based on the accumulated data. It looks for statistical noise over a given period of time and makes changes as needed.
All of these components fall under the umbrella of artificial intelligence (AI). AI, in its most basic sense, consists of computer programs that operate like people. While AI doesn’t necessarily include machine learning or predictive analytics, 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 Orangetheory Fitness, 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. Orangetheory 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 acquiring new leads.
Prior to using AI, the company targeted their spending to women around the age of 35 who had an average income of $80,000 to $100,000. However, through their AI platform they were able to discover a massive following lurking in the shadows — women and men ages 18 to 25. Deeper than that, they uncovered that their audience came from sports (especially college sports) and music backgrounds.
Using these insights, Orangetheory put together a new media campaign called “More Orangetheory, More Life.” Kevin Keith, 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.”
The message spoke directly to the sports- and music-loving audience they were attracting. As a result, Orangetheory grew dramatically in only a few years. It opened almost 1,000 studios in 15 countries and — at the time of this writing — has about 623,000 members.
This is what running an intelligent initiative looks like. Orangetheory didn’t guess what their audience wanted. They used insights gleaned from data to create messages that trigger emotional responses and specific human behavior. But this type of emotional appeal didn’t stop with their advertising. Once a person is in a studio, the fitness giant uses individual data to personalize his or her 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 the guesswork out of understanding what consumers want, you’re able to iterate and innovate faster than before while also mitigating risk. Data as a Service providers help create systems to extract that data and use it to personalize experiences quickly, methodically and strategically.
Let’s take a look at Chicisimo’s machine learning approach. This app helps its users choose daily outfits based on personal preferences. The results are quite subjective, so Chicisimo’s founders knew that they needed to personalize the experience. They turned to consumer data to tailor how each person uses the app.
Because the company didn’t have a problem getting users to sign up and try out the Chicisimo app, they turned their focus to a bigger goal — retaining 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.
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 data about their existing 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 retain new signups by walking them through the steps that were most likely to make them loyal users of the app within the first seven minutes of signing up.
Next, they created behavioral cohorts. Separating users into various cohorts allowed the company to tweak what users see when they first log into the app and more effectively align user behavior with what they see when they log in each time.
Finally, they iterated on how the app learned their users’ 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, grew quickly. By harnessing user data and behavioral patterns, the app was able to scale from zero users to over 4 million in three years. They were 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.
As you might recall from the Retail Systems Research study we showed you above, finding these insights takes skill, infrastructure and expertise — three things that many organizations don’t have. A DaaS provider gets you started by setting up the systems to gather the right data, and then showcases that data in a way that enables you to quickly develop new experience initiatives, as Chicisimo did to spur growth of their app.
One mistake we’ve seen, especially when it comes to running an agile operation, is making decisions based on bias. In a recent BI-Survey, 58% of respondents said that they don’t make data-driven decisions in business but instead base at least half of their regular decisions on gut feelings or experience. Whether we like to admit it or not, 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 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:
We’re not going to dig into all of the intricacies of StoryVesting here 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 — something that’s hard to achieve when you’re focused on the gut reactions of 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. 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.
Walmart is a prime example of this. As Hurricane Frances neared Florida in 2004, Walmart used data-driven decision making when choosing products to stock up on 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, but rather, 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 from their ability to use data to align with customer demands.
DaaS providers help you find areas for growth by taking a methodical, strategic approach to gathering, mining and analyzing data in a way that delivers unbiased insights into what customers really want. In short, outsourcing your data and analytics can 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. Here’s why. When you don’t close the loop, you start to close something else — cross-department communication. Silo walls can be toxic to organizations. When teams don’t collaborate and communicate effectively, they’re less able to tap into the insights data provides them.
Democratizing data is one of the best ways to close out the data loop and distribute 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 need to monitor unique data points and access unique insights when making their decisions. A DaaS provider is skilled in pulling out the most applicable data points to address customers’ biggest concerns. Then, they distribute those data points 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 across the entire organization by giving teams the tools they need to do their jobs exceptionally well. Look at this recent McKinsey study on fueling growth through monetization that illustrates what can happen as you start to infusing these insights into business practices:
You can see that data and analytics have significantly impacted a variety of departments in a variety of industries. This couldn’t have happened if data remained in the hands of 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, 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 information actionable to customers and employees.
For example, a CMO who doesn’t have a solid grasp on how to mine or model data to gather insights about real-time channel sentiment shifts hands data analysis off to the CIO, who really doesn’t understand how to apply the analysis to well-defined ROI models based upon the qualitative data coming in. Similarly, a CXO who doesn’t understand how to develop an accurate data mapping and looping process hands that task over to the CDS, who never gets an accurate view of their employees’ experience due to siloing. Communication barriers between employees (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 that data. Data remains dispersed across departments and lacks cohesion.
The process of pulling out insights becomes smoother when executives are in sync with each other. To be in sync, 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, everyone must be skilled up and in sync so that they can communicate better across the board.
We’re strong believers in the value of skilling up teams. This belief, paired with the fact that few executives and key leaders put aside the time to learn the intricacies of data wrangling, 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.
Stay tuned for updates regarding the release of a powerful workshop, facilitated by us, a DaaS provider, that will teach you how to bring teams together over data and analytics and use the results to fuel your CX initiatives.
The Challenges of Outsourcing Data Services
We offer Data as a Service, but we’re not shy about mentioning both the pros and cons 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 access 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 including dirty data, misinterpretation of complex datasets, and data security issues.
Data Set Hygiene
One large nuance you have to consider when you’re hiring a DaaS vendor is syncing your data across the board. The DaaS provider you hire needs to truly understand how to cleanly sync with other data sets and nodes. We refer to this clean, across-the-board sync as data hygiene.
In our experience working with several Fortune 50 companies we’ve learned that large corporations have a slew of vendors they’re working with. Problems arise as they put outside data prepared by third-party vendors through their own sets of scientific rules and cleaning procedures. They’re now mashing internal data with the provider’s data sets without knowing if they’re comparable or combinable.
Another concern is whether the vendor handles data in accordance with your standards. If you’re a corporate behemoth, you know that your internal data is clean, but how do you know that data from outside vendors is just as clean? And if it is, how do you know whether vendors’ data will blend with your own?
For example, we recently conducted a survey for one of our clients in which we asked the same 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
Although the wording of the questions was identical, we were unable to combine these datasets because customers’ answers differed depending on the timing and the location in which they were asked. If we had combined them, the data would have been dirty and unreliable. Instead, we compared and contrasted the datasets without merging them into a single data.
Data hygiene is a real struggle. It prevents some companies from signing 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. This becomes an issue because DaaS providers need deep access into your database to do their jobs effectively. Sometimes, the differences in data sets means the figures could be skewed. For example, if a vendor shows you that you’re getting a 30% lift but your DaaS provider only shows a 5% lift, there’s a problem.
Data science contains many operational nuances that must be considered to achieve optimal results for you company. If you and your DaaS provider don’t know how to traverse those nuances and govern the data delivery project, you risk spending millions of dollars with the wrong vendor without getting to the insights you need to make actionable and valuable decisions.
Navigating the Complex
One of the biggest challenges to Data as a Service is the innate complexity of data itself. DaaS has been slow to take off because many employees in large corporations, and even in some smaller vendors, don’t have solid knowledge of navigating various datasets. They’re hiring people focused solely on data science or who are 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, while experienced DaaS providers understand that more impactful insights can be gleaned by comparing and contrasting data. Here’s how we compare and contrast paths-to-purchase:
The image above shows 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 to ensure that the provider you hire 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 to navigate 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 fulfill the overall strategies of the company. It’s methodical because it’s nuanced and must work toward a certain objective. At a certain point, one constant question asked by the organization that requires a quick and clear answer from a DaaS provider is, “Is the analysis working?”
For example, was it worth building a $2 billion Hadoop cluster to mine unstructured data from the social sphere? What are your employees saying about it? You want to know that you’re seeing a return on that hefty investment. The right DaaS providers will be able to show you the ROI. Choosing your DaaS partners wisely will ensure that you effectively navigate the complexities associated with data science and see it pay off in actual revenues.
Any time data is transmitted there’s a threat of it being 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 on the DaaS radar. It’s something to take very seriously.
Often, the data being 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 hurt your brand image, but they can also cause you to lose 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:
Threats are becoming increasingly sophisticated, which means cybersecurity and data security measures are more important than ever before. Although the risks are increasing, so is the sophistication of data protection. While it’s important to be aware of the risks out there, 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 regulations 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 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 the next step of outsourcing to a Data as a Service provider, you’ll need to go through an implementation stage. During this time the third-party team will integrate into your internal structure. This is a big next step that should be done methodically. Here’s our roadmap for approaching this new relationship.
As eager as you are to get a better grasp on consumer behavior, demands, and the direction of the market, a relationship with a DaaS provider isn’t something 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 lose valuable time fixing the error instead of finding new insights. It’s equally important to refrain from handing over the keys to any database architecture, another critical piece of your business that requires immediate repair if it breaks.
Instead, start with a small project, such as data visualization.
You went through the process of vetting and getting to know your DaaS provider before hiring them, and now it’s time to see firsthand what the company can do for you. Data visualization, in particular, gives the provider a chance to showcase its data skills. 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 work out any kinks and confirm that you can trust this provider with your valuable data.
Progress Slowly, Beginning with Front-Facing Projects
As you get to know the provider, progress slowly with the complexity and number 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 to optimize it.
A 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’re worth your initial investment by using data to optimize critical conversion funnels.
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 forward and backward, and 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, which is why this component should be the final area you hire a provider to manage.
Effective DaaS providers worthy of your investment shouldn’t be focused solely on systems or on 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 are massive competitive differentiators, 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. the difference 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 the S-curve of business growth, each stage of which requires solid information from the market to understand the best next step to take.
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 when considering scaling 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.
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 analyze it and determine next steps. So far, so good. But what happens next stifles the growth of many small businesses. At that point, a Data as a Service provider should enter the picture.
A DaaS provider can:
- Minimize the risk that a small business will enter analysis paralysis
- Pull out deeper insights than what’s available via Google Analytics
- Propel them out of an innovation loop to the next level of growth
Small businesses fall into an all-too-common trap of getting stuck in the mud while trying to filter all of the insights that pour into the firm. A DaaS provider will pull out the most actionable insights and put them into a data visualization tool to make it easy for everyone in the company to see where the biggest opportunities lie.
As businesses grow, they start bringing on full-time staff. They also start using new platforms to juggle all of the data at their disposal. They now need more and better data.
Mid-tier businesses are poised to be disruptors in the market. These businesses are at an exciting crux in their development. If they can innovate fast enough they’ll propel their brand forward on the S-curve of business growth. If they stagnate because they’re not using the right data, the competition will blow past them.
Data gathered from more advanced platforms becomes less quantitative (and thus, harder to understand) than that gathered from basic platforms. Mid-tier businesses need more qualitative data to understand the human behavior taking place throughout customers’ journeys with their brand. For example, mid-tier companies often use heat maps to understand human behavior on their website, usability studies to find ways to innovate product lines, or NPS scores to gauge sticky points in customer loyalty.
At this point — when companies turn away from simple quantitative insights to bring in more qualitative data — that having a third-party DaaS provider immersed in 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 with human behavior — something that, without expert knowledge, is incredibly difficult to do right, especially when you consider cultural biases.
By the time a company achieves mid-tier status they’ve developed a solid company culture. Employees naturally adopt a cultural lens through which they view data, making it harder to correctly analyze information filtering into the firm. A DaaS provider is instrumental in bridging the gap and offering an unbiased analysis of the market’s voice.
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 have an added component. This is the stage in which Hadoop clusters are built and data scientists 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 available information 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 a silo mentality. Instead of talking across departments, C-Suite executives focus on top-down leadership. It’s rare for a CMO to collaborate regularly with a CIO and COO. This culture of silos leads to a lack of process optimization.
A DaaS provider is expertly equipped to break down those silos and optimize team members. Data as a Service providers are able to keep every team working cohesively toward their North Star Metric. When a large organization is pointed in the right direction — toward growth — teams are better able to come together to pull out and implement new ideas based on market feedback with minimal risk. Working purely internally means running the risk of operating out of silos and never innovating.
Anyone worth their salt knows the importance of using data to run an intelligent, agile operation. We’ve talked a lot about the reasons using data is crucial, and by now you might be wondering if we are one of the “right” Data as a Service providers we keep mentioning. To that end, we challenge you to find out for yourselves by challenging us. Hold us up to your own high standards — we’ll never balk, because it’s what we would do in your shoes. In fact, challenge us at no risk by taking advantage of our discovery assessment.
The first step in working with RocketSource is always a discovery assessment, which is critical to determine 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. Our first step is to discover that stage and determine where we can best fit in to help you simplify the complex.
If you’re ready to do this for your company and 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 them to you (and boy, we wish we could) 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 clients because many are publicly traded companies. We’re a secret weapon of their advanced teams. Our experts know how to hang with 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 good 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.Get Started