It’s hard to pinpoint a particular time when business analytics began. As computing power has improved and more advanced analytics tools have been created, organizations have hired more and more business analysts to help them optimize decisions using data. Google searches for the term “business analytics” have dramatically increased since 2008. Rather than make gut instinct decisions based on “what feels right”, today nearly every company seeks to make “data-driven” decisions, although few actually do this in a rigorous way consistently; and that’s why getting to a place where your firm consistently applies rigorous, efficient, and value-driven analytics can give you a competitive advantage. Value Driven Analytics can help you get there!
With artificial intelligence developing as quickly as it is, many wonder if data analytics can and will be automated. In our opinion, the standard, simple business data analytics some organizations are used to performing can often indeed be automated; on the other hand, proactive and actionable advanced analytics requires complex analytical thinking to be applied to business use cases in creative ways that even the most advanced algorithms still struggle with. Thus, analysts can increase their job security and perform more exciting, engaging work by continually learning the latest analytics tools and techniques. Value Driven Analytics is here to help with our analytics & leadership training.
Value driven analytics is analytics applied to an actionable use case in a robust manner that leads to a higher value decision being made. While it may seem obvious that value driven analytics are the only analytics worth investing in, we’d estimate that, in practice, 50% or more of analytics performed at organizations are actually not value driven! Put another way, we estimate that 50% or more of analytics performed do not actually result in any material value to the organization.
So why do organizations spend so much of their analysts time on non-value driven analytics? Clearly this is not intentional. In our experience, it ends up happening inadvertently for a few reasons:
Value Driven Analytics is unlike any other analytics consulting firm out there in several ways:
Contact us for a complimentary conversation about your data project and you will quickly see that there is something different about Value Driven Analytics!
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. Learn more about analytics transformation here.
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. Learn more about analytics transformation here.
One 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. Learn more about analytics transformation here.
One 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. Learn more about analytics transformation here.
We follow a “bottom-up” approach to analytics transformation that drives change through demonstrating the benefits of adopting more advanced analytics technologies and providing accessible trainings and development initiatives around them, ultimately igniting a will and skill for employees to leap to the next level of analytics maturity.
Our analytics transformation model, which has dramatically enhanced analytics at several organizations, can be summarized in 7 steps:
At a high level, successfully executing on an analytics transformation takes a high level of excellence in 2 key skills that are rarely found together. First, 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 has the analytics leadership that has helped multiple companies execute a successful analytics transformation.
Customer data integration (CDI) uses probabilistic matching to link customer records from multiple systems together into a single ‘global’ customer identifier, giving organizations a 360º view into each customer’s interactions with them. Learn more about customer data integration here.
As mentioned in the “Can data analytics be automated?” FAQ above, it is critical for analysts to continually learn new analytics platforms and tools. Fortunately, there are quite a few ways analysts can do just that!
We highly recommend using case questions to assess a candidate’s knowledge of the analytics tools and techniques required for the role and, most importantly, their analytical thought process. It’s one thing for candidates to walk you through a project they’ve done in the past; most likely, a candidate will have a rehearsed response based hopefully on a project they actually did. But you might get a completely different impression when you ask a candidate to apply an analytics technique listed on their resume to a hypothetical business scenario. After all, it doesn’t do anything for your bottom line if a candidate can tell you 3 parts of a SQL statement. On the other hand, if they can write a SQL query to rank your worst selling products, you’re experiencing first hand an example of what they could do for your organization if hired. Now that’s Value Driven hiring. If you’re not sure where to find or how to interview analytical candidates, we’d love to help with our analytics team services.
Power BI, for many organizations, will have a moderate to significant cost advantage compared to Tableau. Usability differences are hard to quantify and often come down to personal preferences and familiarity, but it is worth noting that the platforms have similar functionality, but work in very different ways; so one’s familiarity with a certain tool will likely play a big factor in which one feels more “usable”. The combination of a generally lower cost and accelerated catch up in functionality seem to have helped Power BI catch up to Tableau recently, even with Tableau’s 12-year head start! Learn more about differences between Power BI and Tableau here.