By getting your organization’s data in order at the beginning of your journey, the entire implementation will be set for success.
With cloud-based applications and their large sources of data having become the norm in the enterprise, there still seems to be a large swath of organizations that have yet to fully embrace the analytics this data can provide. For many, it can be a daunting task to even know where to begin with these massive pools of data, but beginning your analytics journey doesn’t necessarily require a large team or financial resources; starting a data analytics strategy simply takes a true understanding of your data sources.
The best way for a business to get started with data analytics is to get their house in order. What I mean is to take an objective look at their existing systems and processes, both automated and manual, and see where and what core data is being generated and captured. How much data being generated is never looked at today? How many systems are barely hanging together or require extreme amounts of integration and “data wrangling” to generate any type of usable data? How much of what is there today, in terms of systems and reports, are only there “because that’s what we’ve always done”? If this COVID crisis has taught us anything, it’s that new systems and new ways of thinking are no longer optional in many cases. How many different systems does someone need to access to build a simple spreadsheet? How long does that take? How much data manipulation is required to generate the basic reports and KPIs needed to run your business? For most organizations, the answer is “too much!” which is why key reporting and analysis is often done quarterly or monthly.
“Getting your house in order” doesn’t mean tearing everything down, nor does it mean a two year, $10M Total Data Quality Initiative/Master Data Management Marathon, it means making a careful analysis of which systems are core to your business, and which of those core systems still have the scalability and flexibility to perform and generate data in these volatile times. You don’t have to take this entire burden on yourself. Put the onus on your application vendors, and adding more and new vendors is not necessarily the right answer either. You should be looking to make your infrastructure simpler, easier, and cheaper to maintain, and adding new vendors and products to your existing infrastructure rarely can deliver on the promise. I’d look first at your existing vendors that provide and support a broader ecosystem such as Salesforce, Microsoft, Oracle, or Infor, rather than grabbing yet another niche product.
There are countless analytics and reporting vendors out there that will tell you, “Don’t change anything, just put my analytics product on top of your existing infrastructure, and everything will be great!” The greatest house painter in the world can’t fix your leaking plumbing in the kitchen or the mold in your basement. They are only going to cover it up for a while as it gets worse behind the pretty new coat of paint.
Why is this approach effective?
This approach of starting with the core systems, and reducing the complexity of your infrastructure is important, because it saves your organization time and resources, improves your processes, and lowers your costs, even if you never change a single report or spreadsheet. It also provides you the framework to then gain material advantages from your actual analytic efforts. Not having to constantly manipulate data (or at least do a lot less of it) means that your analysts have more time to do their actual job, analyzing data to find “uncomfortable truths” such as three of your top five customers aren’t profitable to you, or that you are incentivizing your sales personnel to sell products that have lower margins or in bundles that actually increase customer churn. It also puts the burden on your application vendors to provide you solutions optimized to delivering data analysis capabilities directly from and across your operational systems, allowing you to leverage investments you’ve already made in training and expertise around your core platforms.
No tool is going to give you “better information” if you haven’t done the work on the back end, so don’t get caught up with which product looks best. Additionally, the most important perspective to get is not from the people whose title is “analyst” or even more important “scientist.” You need to make sure that you get the perspective of the people who go by “manager.” The true benefits of data analysis should come from your rank and file, not specialized data scientists, or even your executives. If your average manager can’t get value from a report or dashboard, you’ve done something wrong.
Another major mistake is to overreach. Too often, I’ve seen organizations decide that “we are going to become a data-driven organization,” so that means everyone needs to be building reports, dashboards, and analytic apps to justify their existence. I recommend taking the exact opposite approach, decide what is most important to your organization, and work to collect the data around that. For example, three years ago when I joined FinancialForce, we recognized that our number one focus needed to be on customer retention; what caused customers to churn, were there particular customer segments that had higher or lower churn, and were there churn indicators we could identify 9 to 12 months before renewals so as to focus efforts where they would make the most impact. We didn’t try to fix everything all at one time, we focused our data collection and analysis around the core issue, and took retention percentage rates from the 70s to the 90s.
Data analytics are one of the best things you can do for your organization, but you’re only as strong as the data you have. By getting your organization’s data in order at the beginning of your journey, the entire implementation will be set for success. In business, information is power, and data analytics is providing information we couldn’t have dreamed of collecting or analyzing just a few short years ago. Companies that view data as a strategic asset and develop robust analytics strategies are the ones that will succeed in this new data-driven world.