Video walkthrough of our seven-step analytics transformation roadmap 

There is no question that data analytics is changing the world and the way we work. Just ten years ago, analysts at many of the largest companies in the world spent hours each week manually extracting data, updating Excel formulas, and e-mailing individual reports to stakeholders. Today, tools like Power BI, Tableau, Python, and more have made this practice much less common. Instead, data analytics is transforming into continually up-to-date, interactive reporting in a centralized online platform that is, perhaps best of all, completely automated; but, for one reason or another, not every company has made this leap to the next level of analytics maturity. In short, they need an analytics transformation.

What is the meaning of analytics transformation and what role does it have?

The definition of analytics transformation in the business world is the process of migrating a company’s analytics capabilities from one level of analytics maturity to the next. An example of analytics transformation could be a firm whose reporting is currently limited to static, manual Excel spreadsheets migrating to an automated, interactive dashboarding solution. Another example of analytics transformation could be a company primarily building reactive reporting and the occasional deep dive analysis introducing more exploratory and proactive data science projects. For many organizations, their analytics transformation could include both of these goals in addition to others like becoming more efficient and increasing analyst engagement.

Analytics is important because it helps an organization make better, data-driven decisions compared to the alternative of gut instinct decisions driven by personal bias. The role of analytics transformation is ultimately to help an organization derive even more value from data through making a higher quantity of higher quality data-driven decisions in a more efficient manner. A company’s baseline level of analytics and their periodic transformation of analytics work together to help the company continually make better decisions. Now that we’ve established that, let’s take a look at how your organization can execute its next analytics transformation in seven sequential steps:

7-Step Analytics Transformation Roadmap

1. Assess current analytics maturity and identify goals
2. Select and implement ideal analytics platforms
3. Execute quick wins
4. Train analysts
5. Create long-term learning initiatives
6. Migrate legacy processes
7. Update analyst hiring and development processes

Step 1: Assess Current Analytics Maturity & Identify Goals

If your organization were to partner with Value Driven Analytics on an analytics transformation, our first step would be to meet with you and understand where you are today on the scale of analytics maturity. We’d ask some questions to understand what’s going well today and what could be improved. Ultimately, we’d want to understand what your goals are for your next analytics transformation:

• Would you like your analytics team to become more efficient, to be able to do more analysis in less time?
• Would you like for the team’s work to be more reliable, less prone to errors and inaccurate data?
• Is there an opportunity to refine the methodology behind analyses to be more robust in its application to the given business challenge?
• Would you like to equip the team to be able to perform advanced analytics projects such as predictive and prescriptive data science model builds?
• Would you like to inspire your analytics teams to identify more exploratory analytics projects proactively?
• Would you like to reduce your dependency on legacy knowledge of a few analysts who might someday leave the organization?

All of these and more can be addressed with a robustly designed analytics transformation; but it’s best to identify your goals up front so that a roadmap can be designed around them. Having executed multiple successful analytics transformations, Value Driven Analytics has the experience to create solutions designed to achieve your goals.

Step 2: Select & Implement Ideal Analytics Platforms

Now that the goals for the analytics transformation have been established, it’s time to identify and implement any new analytics platforms necessary to help you achieve those goals. At Value Driven Analytics, we don’t recommend introducing new platforms unless there are truly significant benefits to doing so. For instance, if a company is already consistently using R for advanced analytics, we’d be very hesitant to recommend switching to Python. On the other hand, if a company is currently updating Excel reports manually and e-mailing them out, we would recommend introducing Power BI or Tableau in order to benefit from the automation and interactivity they provide.

In some cases, the ideal analytics transformation could result in some slight to moderate increases in the organization’s analytics software costs; but did you know that, in some cases, the transition to more advanced analytics tools, ones that will increase analysts’ efficiency and widen their capabilities, can actually reduce the organization’s analytics software costs? In some cases, the savings can be quite significant! Value Driven Analytics will be able to give you an idea up front of what your organization can expect based on the tools you’re currently using and your analytics goals. It is important to note that we do not accept any kind of referral fees from analytics software providers. In fact, we often recommend high quality, open source solutions that save your organization money, while increasing flexibility.

Whether your organization needs to introduce a new interactive dashboarding tool, start using SQL for efficient data manipulation, or implement automation and data science through Python on a server, Value Driven Analytics can help you through the process.

Step 3: Execute Quick Wins

As mentioned in our analytics transformation product page, when it comes to analytics transformation, we follow a “bottom-up” approach that drives change through demonstrating the benefits of adopting more advanced analytics technologies. Executing some quick wins is a great way to demonstrate the purpose of the analytics transformation to the organization’s analysts (and management!). After seeing a few examples of how the new tools and techniques introduced are helping the organization drive more value with data (and cutting down on work time), the vast majority of analysts will be sold on the changes and many will be inspired to start researching the tools themselves and start applying them to their projects.

But, with so many legacy processes, how should an organization go about prioritizing “quick win” opportunities? Value Driven Analytics can work with your organization to identify what quick wins are possible and how long they would take to implement. This is the reason that our analytics transformation package comes with 2 automated data processes, 8 interactive dashboard builds, 5 actionable analyses, and 5 data science model builds. We can help you execute these quick win projects, and mentor and ultimately inspire your analysts in the process.

Step 4: Train Analysts

The fourth step of training analysts in any new analytics platforms implemented and in more advanced analytics techniques is so critical. Training analysts is ultimately where sustainable transformation occurs.

As you can imagine, learning new analytics tools and techniques requires excellent analytics change management. It’s important for current analysts to understand that the data analytics transformation facilitates long-term career development by helping them learn new analytics skills. The standard, simple business data analytics some organizations are used to performing can be automated, but the proactive and actionable advanced analytics that comes with analytics transformation require complex analytical thinking to be applied to business use cases in creative ways that even the most advanced algorithms still struggle with. Thus, rather than put your current analysts jobs at risk, analytics transformation in the business world can actually help create more job security as long as the analyst is willing to learn.

In this step, it is absolutely critical to have an analytics expert who 1) knows the analytics tools and their capabilities inside and out, 2) is an expert at troubleshooting not only their own code, but others who will be learning, and 3) can creatively and quickly apply these analytics tools to the organization’s opportunities. With over a dozen years of varied and robust experience in analytics, Value Driven Analytics is the ideal analytics expert to lead your organization through its analytics transformation. You can learn more about our analytics & leadership training services. Our analytics transformation package comes with 20 group training sessions (over 50 hours) on topics like SQL, interactive visualization (Power BI or Tableau), Python, analytics techniques, data science algorithms, attention to detail, leadership, efficiency techniques, project management, and communication for as many employees as you see fit. We’ll work with you to customize the trainings offered to what will be most relevant and important for your analytics team’s success.

Step 5: Create Long-Term Learning Initiatives

Training analysts is a necessary first step in their learning journey; but most analysts learn far more while subsequently applying the concepts to real data on their own. That being said, it can be daunting to know where to start and, in particular, difficult to troubleshoot when the analyst hasn’t had much time using the platform. This is where long-term learning initiatives come in.

With the Value Driven Analytics analytics transformation package, we help launch and facilitate (for the first 4 months) ongoing development initiatives including monthly analysis meetings, weekly office hours, a 1:1 mentorship program, and more; for each of these, we recommend that organizations designates a “mentee” to eventually lead the initiative.

• Monthly analysis meetings involve getting analysts throughout the organization together to see 3-4 analysts present an analysis, tool, or technique that could be useful to other analysts; getting visibility into analytics approaches other analysts have taken is one of the few ways analysts can gradually enhance the rigor of their analytics methodology
• Weekly office hours is a time where analysts can optionally meet with an analytics expert who can help them brainstorm analytics projects and approaches or even troubleshoot their code with them
• A 1:1 mentorship program is a an opportunity for volunteer experienced analytics mentors to regularly provide project-based advice to 1 (or more) mentees for a temporary duration

Step 6: Migrate Legacy Processes

Now that your analytics team is really starting to ramp up in the new analytics platforms and techniques, it’s time to migrate any legacy processes to the new platform as needed. These are quite a few ways to prioritize which processes should be migrated first.

Our recommendation is to start by building any new analytics projects in the new tools; this prevents even more “technical debt” from being created. There will naturally be a period of time where some analytics projects are executed in a new platform and some in the old, which is just part of the transition.

One side benefit of the migration to a new tool is that it’s a great opportunity to rationalize which legacy analytics projects are still needed. It’s a new beginning of sorts, which also creates an opportunity to perform any reworking or standardization your team has been wanting to do. In time, analysts will gradually migrate rationalized legacy processes to the new platform. You could also consider hiring an analytics consulting company like Value Driven Analytics to migrate the processes for you so that your team isn’t burdened with it.

Step 7: Update Analyst Hiring and Development Processes

Now that your organization is using some new tools for analytics, you might want to consider updating your job postings and even your interview questions/process. Perhaps, through the analytics transformation process, you’ve raised the bar for what a successful analyst needs to be able to do, and you’d like to maintain that bar for future hires. Value Driven Analytics can make recommendations in both of these areas. In particular, we’d recommend using case questions to assess candidates’ knowledge of the new tools and techniques your organization is using and, most importantly, their analytical thought process.

At the same time, it’s a great time to reflect on your organization’s internal analytics development processes. After all, you’ve invested in teaching your team state-of-the-art analytics skills. Creating a robust development process will make sure those skills stay sharp and, most importantly, that your analysts will want to continue to grow at your company.

Final Thoughts

If you’ve made it through all seven steps, congratulations! You’ve done something that many organizations talk about, but that few successfully execute. If you’re just beginning to think about your next analytics transformation journey, please don’t try to do it alone!

Successfully executing on an analytics transformation takes a high level of excellence in 2 key skills that are rarely found together. First, as mentioned in step 4, it takes an analytics expert skilled in all things analytics who can, during every step, help make smart decisions and handle any issues that come up. Second, it takes a next level leader who has the passion and wisdom to handle the project management and change management aspects of the transformation.

Value Driven Analytics is the analytics leader that can help your organization truly achieve the analytics transformation you’ve always dreamed of. We have successfully executed multiple analytics transformations, and, to date, haven’t failed yet. With Value Driven Analytics helping your organization execute the seven steps outlined above, you too can experience analytics transformation.