Crack the Code to Analytics at Scale

While many organizations are investing in analytics in some form or fashion, very few broke away from the rest and have mastered the skills of running Analytics at scale. Okay, that’s great to know, but the question remains, how did such organizations outperform their competition?

If we look closely at what would it take to perform analytics at scale, I would argue that for a business to set itself apart and crack the code of analytics at scale, it needs to significantly invest and outperform in few critical areas. In the next section, I will attempt to address those areas and provide examples. I’m interested in your feedback and thoughts on those.

– One of the most important areas in this process is strategy alignment by weaving analytics into critical functions in the fabric of the company. Functions that span across the business. For example, while investing in analytics to work on an island can have limited success in some areas; it won’t scale across the enterprise.

To scale beyond your piloted island, you are going to need a cross-functional business strategy and vision with commitment and alignment from the leadership team. By leadership team, I don’t mean just C-Level leaders, but this commitment and vision needs to trickle further down the management ladder, most importantly, middle management type leaders.

– Next area is obviously something businesses try to avoid and that is to increase analytics spending and investment. I believe as much as we do not like this point, no one can argue this is an area that is critical to arriving to analytics at scale. However, I do not mean simply spend more money without strategy (remember first area). While it is a myth to “drink responsibly”; it is quite the opposite when it comes to investment in analytics at scale. The bulk of the investment should be directed towards embedding analytics into all business core decision making processes and workflows. This is one reason we recently started seeing many companies announce doubling down on analytics spending.

– In the midst of all of this, we should not forget about a clear data strategy. We know that not all data is created equal. Organizations looking to run analytics at scale need to have the accountability clearly defined for their data. Data can be segmented into categories that can be easily identified and serviced based on the use case for which the data is going to be consumed. For example, you need to govern the same patient dataset differently whether it is being consumed by a highly sensitive workflow or you are simply using it for marketing purposes. This is why it is critical to identify and categorize the data so you are better positioned to service it based on its value rather than treating all data equally.

– The art to continue developing analytics models and methodologies to extract more insights from the data and deploy those learnings back into the enterprise. If you want to crack the code to analytics at scale, Organizations need to continue to challenge their existing data sources with new and more sophisticated analytics modeling to test the quality of the data and to potentially find alternatives that can provide better insights.

– We should not forget the human factor in all of this. Organizations that are looking to run analytics at scale need to have expertise in areas like data science, engineering, architecture, and transformation. The industry average calls for 20 analytics professionals per 1000 full time employees. Some organizations do less, some run more, but the ones looking to run at scale tend to have this number much higher. I don’t have studies to give out a number, but I would expect this number to be in the 60s and more. Companies also need to have a strategy on retaining and attracting this type of talent. I could think of an initiative to have something like an innovative analytics centers to nurture such talent be a great strategy on the path to analytics at scale.

– Again, human factor is a key. However, it is critical to create cross functional teams to create innovation throughout the organization. I’m not simply talking technical experts only, but this team needs to include representatives from the business, UXD (User Experience Design) expertise, and data scientists. Such diversity in this cross-functional team will help reduce falling into the trap of creating an isolated silo project but rather deliver an end-to-end value add use case.

While some can come up with more items and factors, I would argue that while the above list provides a strong strategy to running true analytics at scale; there is one last very critical and important differentiator to those enterprises that were able to crack the code to scale their analytics systems and take advantage of unlocking the value of their data by making data work for them and not falling victims into working for their data.

I have one word to describe this, “Embedded Analytics”, okay two words. Enterprises need to embed analytics into their decision-making process and to do this in such a way that is user friendly and tailored to each team/group making those decisions (for example, an executive leader, sales representative, field specialists …). The list spans catering the right tool for the right resources, such as, mobile apps, dashboards, recommendation process … etc.

This is where enterprises are challenged with turning insights into outcomes. This is where all that hard work summarized by the above steps pay dividend and where the value of the analytics journey is truly extracted.

After doing all of that, the last area where enterprises are still challenged with is embedding analytics into the corporate fabric and culture at all levels.

Without mastering and implementing this last step, enterprises miss out on scaled analytics and will end up with missed-value analytics. Don’t get me wrong, they will realize benefits of the analytics project, but not enough to justify the investment and not enough to scale across corporate fabric. This is where I have seen most analytics projects fall short and end up withdrawn and sent to the chopping block for the hopes and promise of the next silo analytics project.

Following the steps above would get you to not only have your cake but allows you to eat it as well.

Interested in feedback, thoughts, and a good positive challenge to what I have laid out here …

I would encourage you to reach out to your local Hitachi Vantara Team to learn more about how together we can co-create your next analytics journey. Together we can build a joined strategy to the path of analytics at scale.