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Building a Data Analytics Roadmap? Answer These 3 Questions

By July 29, 2020 No Comments
3 Questions to Help You Build Your Analytics Roadmap

3 Questions to Help You Build Your Analytics Roadmap

In our experience, many analytics projects have the right intentions such as:

  • A more holistic view of the organization
  • More informed decision making
  • Better operational and financial insights

With incredible BI and analytics tools such as Looker, PowerBI, and Tableau on the market, it’s tempting to start by selecting a tool believing it to be a silver bullet. While these tools are all excellent choices when it comes to visualization and analysis, the road to successful analytics starts well before tool selection.

So where do you begin? By asking and answering a variety of questions for your organization, and building a data analytics roadmap from the responses. From years of experience, we’ve seen that this process (part gap analysis, part soul-searching) is non-negotiable for any rewarding analytics project. Give the following questions careful consideration as you run your current state assessment:

How Can Analytics Support Your Business Goals?
There’s a tendency for some stakeholders not immersed in the data to see analytics as a background process disconnected from the day to day. That mindset is definitely to their disadvantage. When businesses fixate on analytical tools without a practical application, they put the cart before the horse and end up nowhere fast. Yet when analytics solutions are purposeful and align with key goals, insights appear faster and with greater results.

One of our higher education clients is a perfect example. Their goal? To determine which of their marketing tactics were successful in converting qualified prospects into enrolled students. Under the umbrella of that goal, their stakeholders would need to answer a variety of questions:

  • How long was the enrollment process?
  • How many touchpoints had enrolled students encountered during enrollment?
  • Which marketing solutions were the most cost effective at attracting students?

As we evaluated their systems, we recognized data from over 90 source systems would be essential to provide the actionable insight our client wanted. By creating a single source of truth that fed into Tableau dashboards, their marketing team was able to analyze their recruiting pipeline to determine the strategies and campaigns that worked best to draw new registrants into the student body.

This approach transcends industries. Every data analytics roadmap should reflect on and evaluate the most essential business goals. More than just choosing an urgent need or reacting to a surface level problem, this reevaluation should include serious soul-searching.

The first goals you decide to support should always be as essential to you as your own organizational DNA. When you use analytics solutions to reinforce the very foundation of your business, you’ll always get a higher level of results. With a strong use case in hand, you can turn your analytics project into a stepping stone for bigger and better things.

What Is Your Analytical Maturity?
You’re not going to scale Mt. Everest without the gear and training to handle the unforgiving high altitudes, and your organization won’t reach certain levels of analytical sophistication without hitting the right milestones first. Expecting more than you’re capable of out of an analytics project is a surefire path to self-sabotage. That’s why building a data analytics roadmap always requires an assessment of your data maturity first.

However, there isn’t a single KPI showing your analytical maturity. Rather, there’s a combination of factors such as the sophistication of your data structure, the thoroughness of your data governance, and the dedication of your people to a data-driven culture.

Here’s what your organization can achieve at different levels of data maturity:

  • Descriptive Analytics – This level of analytics tells you what’s happened in the past. Typically, organizations in this state rely on a single source system without the ability to cross-compare different sources for deeper insight. If there’s data quality, it’s often sporadic and not aligned with the big picture.
  • Diagnostic Analytics – Organizations at this level are able to identify why things happened. At a minimum, several data sets are connected, allowing organizations to measure the correlation between different factors. Users understand some of the immediate goals of the organization and trust the quality of data enough to run them through reporting tools or dashboards.
  • Predictive Analytics – At this level, organizations can anticipate what’s going to happen. For starters, they need large amounts of data – from internal and external sources – consolidated into a data lake or data warehouse. High data governance standards are essential to establish consistency and accuracy in analytical insight. Plus, organizations need to have complex predictive models and even machine learning programs in place to make reliable forecasts.
  • Prescriptive Analytics – Organizations at the level of prescriptive analytics are able to use their data to not only anticipate market trends and changing behaviors but act in ways that maximize outcomes. From end to end, data drives decisions and actions. Moreover, organizations have several layers of analytics solutions to address a variety of different issues.

What’s important to acknowledge is that each level of analytics is a sequential progression. You cannot move up in sophistication without giving proper attention to the prerequisite data structures, data quality, and data-driven mindsets.

For example, if an auto manufacturer wants to reduce their maintenance costs by using predictive analytics, there are several steps they need to take in advance:

  • Creating a steady feed of real-time data through a full array of monitoring sensors
  • Funneling data into centralized storage systems for swift and simple analysis
  • Implementing predictive algorithms that can be taught or learn optimal maintenance plans or schedules

Then, they can start to anticipate equipment failure, forecast demand, and improve KPIs for workforce management. Yet no matter your industry, the gap analysis between the current state of your data maturity and your goals is essential to designing a roadmap that can get you to your destinations fastest.

What’s the State of our Data?
Unfortunately for any data analytics roadmap, most organizations didn’t grow their data architecture in a methodical or intentional way. Honestly, it’s very difficult to do so. Acquisitions, departmental growth spurts, decentralized operations, and rogue implementations often result in an over-complicated web of data.

When it comes to data analysis, simple structures are always better. By mapping out the complete picture and current state of your data architecture, your organization can determine the best way to simplify and streamline your systems. This is essential for you to obtain a complete perspective from your data.

Building a single source of truth out of a messy blend of data sets was essential for one of our CPG clients to grow and lock down customers in their target markets. The modern data platform we created for their team consolidated their insight into one central structure, enabling them to track sales and marketing performance across various channels in order to help adjust their strategy and expectations. Centralized data sources offer a springboard into data science capabilities that can help them predict future sales trends and consumer behaviors – and even advise them on what to do next.

Are you building a data analytics roadmap and are unsure of what your current analytics are lacking? Aptitive can streamline your search for the right analytics fit. Our 90 minute data architecture assessment helps you connect your current state with your analytic goals.