Data Analytics Maturity Transformation Roadmap | A Data Strategy

Does your organization need an analytics transformation? Hi, I'm Brian Amrein, co-founder and principal analytics consultant at Value Driven Analytics. Today, I'm going to talk to you about the process of going from one level of analytics maturity to the next. Perhaps you're an organization that today primarily sends out reporting through Excel, it's manual to update, it could take hours to update each week, and it's static, you can't drill into it. Imagine going from an environment like that to an organization who's using an interactive dashboarding platform, where all of the dashboarding updates are automated, so you can have the latest data every single day automatically with no manual work. Imagine being able to drill into those reports on specific geographies or specific business segments without having to go to an analyst to do that every time. And imagine having reporting that is insightful, that's truly tailored to the business problem. On top of that, imagine not just having reactive reporting that looks at the past, but proactive analysis done using advanced analytics techniques that automatically detect relationships and trends in your business. Wouldn't that be great? Well today, I'm going to be walking you through our seven-step roadmap for doing just that, achieving nothing less than an analytics transformation. To do that, we're going to take a look at our roadmap on our website, ValueDrivenAnalytics.com. I'll see you there. All right, here we are on ValueDrivenAnalytics.com, and the first thing we're going to do is head to our analytic services and then go to analytics transformation. Now, before we get into our seven-step roadmap, I just want to give you a high-level overview of our approach to analytics transformation. As you can see here, our approach is a bottom-up approach. We start with the analysts themselves that are going to be doing the work. This is not a top-down directive. That's not the way we typically approach analytics transformation, although it's certainly good to have support from executives for encouraging analysts to use more advanced analytics platforms. That is certainly helpful, but our approach focuses first and foremost on actually performing true advanced analytics, actually building interactive dashboards, showing analysts the benefits that using more advanced analytics tools can provide and more advanced analytics techniques. Now, what that does is eventually they start to see the benefits compared to using maybe traditional techniques in the past, like Excel for things. They see the automation that can lead to a better work-life balance. They see the neat insights you're able to get by using machine learning models, for instance, and that ignites a will to learn those tools themselves. Well, now that they have the will to learn, it's time to actually teach them, and that's where the trainings come in. That's where the analytics development initiatives, like mentorship programs and analysis meetings, even office hours where they can actually go and get help when they run into issues as they try to learn these new techniques, and so we're not only giving them the will, the desire to learn by illustrating the benefits, but we're also giving them the skill and the resources to learn. When you combine those things together, pretty soon you're going to see nothing short of an analytics transformation. Now, let's walk through our seven-step roadmap for analytics transformation by clicking on the link right here. All right. In this article, you will see a thorough explanation of the seven steps that we go through when we're performing an analytics transformation. As you scroll down, you'll see a step-by-step guide, details on each one of these steps. We're going to start by walking through each one of the steps in this visual. The first step in an analytics transformation is to identify your current level of analytics maturity, and more importantly, identify your goals for your next analytics transformation. Now, on the first item, we actually have an analytics transformation assessment that you can take for free on our website, so just click on this link right here to get to the analytics transformation assessment. It'll ask you about 30 questions, and when you submit this within a few days, you should get a report that'll, one, tell you what your current level of analytics maturity is, and two, more importantly, give you some advice to get to the next level, so please check that out. Now, we can help provide that assessment for you by interviewing your analytics team and your stakeholders as well. That'll be part of the analytics transformation process, but again, the most important part is to identify your goals. Now, there are a lot of reasons that an organization might want to lead an analytics transformation. It could be that you want your analytics team to become more efficient, and there's several ways to do just that. One is to use more efficient analytics tools, coding tools where an analysis that's coded out can be reused, even visualization tools where the refresh is automated and the stakeholder can directly themselves drill into trends instead of having to ask an analyst to do all those things to refresh data and to filter it down for them, so there's a lot of ways to use analytics tools to become more efficient, and then there's also techniques that an analyst can use to be more efficient in their work as well, and you have a whole training on efficiency techniques, so we would approach helping a team become more efficient both of those ways using more efficient tools and more efficient tactics and techniques throughout the day. Now, another big priority for an analytics transformation might be to lower the risk of attention to detail issues. Maybe you're facing cases today where you've made bad decisions because an analysis wasn't conducted in a robust way or it was made based on inaccurate data. If that's the case, please let us know and we can help you with that. We have a whole training on YouTube that shows how to lower the risk of making attention to detail errors and improve an analyst's rigor of their methodology, so we can help with training around that and putting in place more robust processes to avoid those things. You may also have the goal of helping your analytics team be able to perform more advanced analytics that's automated and that helps you identify things proactively that can help your business, so in that case, it may be a training need. Maybe you need to learn tools like Python and SQL that'll equip you to build data science models, and then on top of that, learn data science techniques and algorithms. Well, value-driven analytics can help you do both of those things and then ultimately inspire analysts to do more of that. What are some ways to keep your analytics team engaged? When analytics teams are more engaged, they are more likely to proactively pursue those analyses and proactively recommend analysis that could help your stakeholders solve their business challenges. In order to motivate that, they've got to be engaged, and one of our services that we would provide during the analytics transformation process would be to provide consulting on ways to help your team be as engaged as possible. Finally, you may be in a situation today where your entire business relies on the legacy knowledge of a few analysts, and that is certainly not ideal. You want to have processes that are sustainable, processes that are simple to understand, that a new analyst could understand. It doesn't depend on having a lot of legacy knowledge, and so documentation can be part of that, but also just setting up processes in a really simple and easy to learn manner, and that's something we provide consulting on as well. So all of those and more, there could be other goals that you have for your analytics transformation. That is where you start. Step two in the analytics transformation journey involves selecting and implementing the ideal analytics platforms for your organization, and it does indeed depend on your organization. We're looking to find tools that are going to help your team become more efficient, but also more flexible to be able to perform the kinds of analysis and model builds that your organization desires. First of all, we're going to probably look for a SQL tool for your organization to use if you're not already using one. Now we're not going to recommend a new tool unless it's actually going to help you. For instance, when it comes to interactive visualization, we'll take a look at what you're currently using. You may not be using a tool at all. In that case, we will certainly recommend a tool for you, most likely Power BI, which tends to be less expensive, but actually really easy to use compared to other tools. It's one of the most popular tools out there, and in fact, for some organizations who have an E5 Microsoft license, the Pro license may already be included. I want to show you some trends in the industry around interactive visualization tools. These are two of the most popular tools today, Power BI and Tableau, and what you'll notice is Tableau has been the industry leader for a long time, and at least in terms of Google searches, which is what we're looking at here, has been outperforming really any other tool that you would look at for a while. Now, Power BI, as you can see, around 2015 started to come out and launch more and more updates over time. I mean, they were launching, and may still be, monthly updates to the platform to add new functionality, and what you'll see is they clearly have the momentum, and just recently, around maybe May or June of 2023, it's actually starting to see more Google searches for Power BI and for Tableau for the first time, so there's some really interesting trends happening there. Now, if your organization is currently using Tableau, but again, you maybe have an E5 Microsoft license, there is a huge cost savings opportunity to migrate from Tableau to Power BI, and value-driven analytics can help you with that. I think a lot of organizations are looking at that right now, but we can help you make that assessment. Again, we are not going to recommend a new analytics tool unless it's really going to be worth it. Now, another tool, especially if you're looking to get your analytics team to do more advanced analytics using data science techniques, they're going to need to know SAS, Python, or R to really be able to do that effectively and efficiently. I wanted to show you some trends in the industry around that as well. Now, the comparison is tough, especially when you bring R into the mix, because R is simply a letter, but it's also a pretty popular analytics platform, so what I've tried to do here is say Python analysis. I think this is good because Python is very flexible. It's a general-purpose language. People are using Python for a lot of things, not just analysis or data science, so I'm searching for Python analysis, R analysis, and SAS analysis, trying to keep things on the same level here. What you'll notice is SAS, even further back, it's been around for a long time, since the 1970s. It's one of the original advanced analytics tools, tools for analytics in general. It came out very strong, but it has been declining in popularity. It has a significant cost versus these other tools. R and Python are open source, but they have lots of functionality. It's pretty impressive. As a result, the usage of SAS has been declining over time. It's expensive. People have just moved away from that. Originally, a lot of people were moving toward R as a SAS substitute that was free and open source, but what you'll see is over the last 10, 15 years or so, Python has become more and more popular for analysis and especially data science. Our recommendation would be, if you're not using an advanced analytics tool at all today and you're trying to get into an advanced analytics tool, would be to go with Python, the tool that has the momentum and the tool that is very flexible as a general purpose language. It's pretty easy to learn. We can help your team learn that. We have lots of training around that. That's the tool we recommend. Now, you may also be one of these companies who wants to move away from SAS to save some money and use more Python. We can help you with that migration as well. Now, if you're using R today, would we say it's worth it to move to Python? Probably not. Again, we only want to recommend going to a new tool if it's actually going to be worth it. Coming back to step two, those are some of the ways that we would assess what platforms you're using today and where you might be able to see significant gains, whether it's by monetary savings or by increases in your functionality and your efficiency through moving to a new analytics platform. Step three in the analytics transformation process is to execute quick wins. Remember back to the beginning of this video where we talked about igniting a will, a desire among your analysts to switch to more advanced analytics tools and techniques. Well, in order to do that, you need to show them the benefits. In order to show them the benefits, you actually have to execute some quick wins. This is where value-driven analytics would get involved and help you do just that. Now, our analytics transformation comes with several different quick wins, so to speak. First is it comes with two automated data processes, and we would talk with you to determine which automated data processes are most important for your organization. It could be a customer data integration implementation. It could be an efficient ETL implementation that will save your analysts time when it comes to creating new data tables. It could be that your team needs automated data validation checks to alert you proactively when something's off of the data. Whatever makes sense, we can implement two automated data processes for you through our analytics transformation service. It also comes with eight interactive dashboard builds, so you may want to track sales over time and be able to drill into sales. You may use one of the eight on something like that. You may have an initiative that you need a dashboard built for and use one of your interactive dashboard builds on that, but analytics transformation comes with eight of those, so you're going to have a lot of opportunities and a lot of dashboards so that analysts at your organization can see the values, see how they're automated. You don't have to put manual work to refresh it each week, and stakeholders can drill into the data themselves. We don't have to do all that work on our own. That gives you more time to do more proactive, advanced analytics, and then the analytics transformation service also comes with five actionable analyses. Now with all of these, we recommend assigning a mentor. We can actually take one of your analysts or several of your analysts along with us as we create these so that they see hands-on how to actually create some of these things. When it comes to actionable analyses, the rigor is extremely important, so we'll show how to conduct a rigorous analysis. Maybe you need us to dive into a sales trend you're seeing and really figure out what's driving that. Maybe you have a lot of programs that you'd like to assess the ROI on, and that's one of the five analyses that we take up. Then finally, a lot of organizations want to do an analytics transformation because they'd like to see their analytics teams doing more advanced analytics, and so we can help your team and bring them along implementing five data science models. Maybe you need a prediction of sales, maybe you need sales force quotas, or you want to understand what the biggest drivers of your sales are. Whatever it is, our analytics transformation service comes with five data science model builds. It is a great value, and imagine all of this being done and your analytics team seeing the benefits of using more advanced analytics skills and what that would do for their will, their desire to learn these tools themselves. Step four in the analytics transformation process is to train your analysts. This involves giving them the skill to themselves be able to use more advanced analytics platforms and techniques. One of the things that I think is really neat about our analytics transformation model, it doesn't require layoffs or new employees, it just requires taking your existing employees and developing them to be able to operate at the next level of analytics maturity. It's a development initiative for them, which should actually result in more engagement, which could mean that they're now proactively doing more analysis themselves. Now I'm going to scroll down to section four here, step four, just walk you through what our analytics transformation service involves. It actually includes more than 50 hours of analytics training on topics like SQL, interactive visualization, whether it's Power BI or Tableau, Python, analytics techniques, data science algorithms, training on attention to detail, soft skills like leadership, efficiency techniques, project management, communication, and more. So your analytics transformation service comes with all of that training to make sure your analysts are operating at a really high level of efficiency, productivity, and effectiveness. At the end of the day, using analytics to make the most value for your organization. Step five in the analytics transformation process involves creating long-term learning initiatives. So step four was to initially train analysts in these more advanced skills and platforms, but now we want to make sure that's sustainable. What about new analysts? How do your existing analysts continue to learn and share their knowledge with each other to really see exponential returns and exponential learning in this analytics transformation process? Well, I'm going to scroll down to step five here and we can get a little bit more information. Now our analytics transformation service actually comes with the creation of, in four months facilitating, three analytics transformation learning initiatives. Ongoing learning initiatives. So first would be a monthly analysis meeting. This would be monthly where analysts at your organization are getting together to share analysis with each other. So one analyst on the finance team might have an idea or a project that they've actually done that could inspire someone on the sales team to use that same technique for a business challenge in the sales area. So knowledge sharing is really important and so that's something the monthly analysis meeting would facilitate. Another initiative that we would help start is a regular office hours. This is a meeting where anyone can drop in, completely optional, but analysts from around your organization would drop in and ask questions. Maybe they're troubleshooting some Python code, they just learned Python, they're trying to use it, but they're running into errors. They need someone, a meeting where they can go and get help working through that. That's exactly what weekly office hours is all about. So we would help you implement that. Finally, we also would help you implement a one-on-one mentorship program. Now this could be a mentorship between analysts at your organization or mentorship between an analyst, an experienced analyst, and someone who's aspiring to become an analyst. Maybe someone on another team who wants to eventually become an analyst. They can bring them together and allow them to learn from each other. So a one-on-one mentorship program can really help the engagement on both parties. A development initiative for the analyst and an opportunity to learn analytic skills for the non-analyst. Now with all of these, we would help start the program, we would facilitate them for four months, but what we recommend is assigning a mentor to each one of these so that eventually after the end of that four-month period your analyst will be able to take these on and lead them and continue them from there. The next step in the process is to migrate legacy processes to your new analytics platforms. So for instance, if you have a lot of reporting in Excel and you want that to become automated and interactive, you might move that into a platform like Power BI. Now we can get involved and help you do that so your analysts aren't burdened with that on top of their regular responsibilities. We're happy to do that, but it's really important to do that over time. Where you would want to start is to create all new reporting and all new analysis in the new platforms and your management should encourage that. Are you using Python? Are you using dashboards? Hey, when you build this report, can you use Power BI for it? But eventually you'll want to migrate old processes to that new platform as well. Now this is a great opportunity to rationalize old reporting. Maybe there's some reporting that's not needed anymore, maybe there's an opportunity to standardize the look and feel of your reporting through this process, and maybe there's some opportunities to enhance it. Maybe you don't build it in the exact same way you did before, but just in a new platform with some additional filters, but you really think about what the best design is to get the most value out of that analytics asset. So that's an important step of eventually over time migrating legacy processes. The last step in the analytics transformation process is to update your analyst hiring and development practices. Now that your expectation and your bar has been raised for analytics, you need to make sure that new analysts coming to the organization have the skills that you're now using. They have some experience with interactive visualization, they have some experience with data science perhaps. Think about what the bar looks like for your new analyst now that you're at this next level of analytics maturity, and I highly recommend using case questions to assess whether they have a robust ability to apply SQL or Python or a dashboarding tool to a business problem. So that's something we would help you with during the analytics transformation process is designing analytics case questions to make sure you really get analysts that know these skills when you look to hire in the future that are going to fit in with your new level of analytics maturity that your organization is now at. It's also a good time to reflect on what your current development resources and development looks like for your current analyst. Now that they're at this new level of analytics maturity, it's important to help them continue to grow. And like we talked about, these long-term learning initiatives that would be put in place like analysis meetings and mentorship programs, those will help with that. But think about what career trajectory looks like in your new analytics maturity stage. And that's what it takes to accomplish an analytics transformation. You have to start with assessing your current level of analytics maturity and identify your goals for that analytics transformation. Then you need to select and implement the right analytics platforms for your organization. You need to execute quick wins in those platforms and using those new techniques so that other analysts can see the benefits and create a desire within them to learn those as well. Then you have to teach them the skills and facilitate their development as they learn to apply analytics in these new platforms and using these new techniques. Then you need to create long-term learning initiatives to keep the learning going. You need to migrate eventually your legacy processes to these new platforms. And finally, you need to assess your current analyst hiring and development processes to make sure they reflect their new level of analytics maturity. And that's what it takes to accomplish analytics transformation. You know, analytics transformation is something that a lot of companies talk about, but few actually successfully implement. It takes a person who is an expert in all things analytics to guide the organization and handle all the troubleshooting and technical issues that come up along the way and train the rest of the organization how to implement those tools as well. But it also takes a leader who has the passion and the wisdom to handle the project management and the change management aspects of analytics transformation as well. If you're considering going on an analytics transformation journey, please don't try to do it alone. Partner with Value Driven Analytics. We have successfully implemented multiple analytics transformations and we'd love to help your organization do the same. I want to thank you for watching today. I hope you got a lot out of it. And if you did, please consider liking the video and subscribing if you'd like to see more videos like this in the future. Now, if you have any questions about what we covered, please consider leaving a comment. I'll try to respond to it as soon as I can. One last thing, if you or your organization ever need anything analytics related, an analysis, a dashboard build, maybe a data science model build, perhaps deeper training on this very topic or other analytics topics, even a complete analytics transformation, please consider Value Driven Analytics. Check out our at valuedrivenanalytics.com. And if it resonates with you, give us a call. We'd love to help you out.