August 22, 2018
Anthony Wong

The Data Analytics Life Cycle

Your data is important, do you understand it?

Your data is important. If you agree continue reading…

With that out of way, many businesses don’t know how to start down the path of data enlightenment. How do I get the data from all my systems? Do I have enough data? Do I hire a team of data analyst and data scientists? Who is going to lead this data team? Finance? IT? CEO? Strategy? I’m looking at it but what does the data say? Should I invest in machine learning and AI?

Like all good answers in business — it depends on your situation. However, answering these questions about data is easier than ever. The data software solutions from datawarehousing-as-a-service providers like Amazon Redshift or Snowflake, ETL solutions like Stitch and Fivetran, and data visualization stalwarts like Tableau are becoming common place in high growth startups. It takes less investment than the 6 figure investments that were previously required for “old school” data solutions (see SAP, Oracle, etc). 

The beauty of these solutions are that they are able to “drop-into” your current tech infrastructure. Whatever system you are on, these solutions can plug in with minimal $ investment or implementation time. Once you understand the solution set, your data ecosystem can begin informing you about your business. 

Data Analytics Life cycle — Hindsight, Insight, Foresight

Source: Gartner

Hindsight - What Happened? It’s hard to admit but what we’ve seen is that many companies don’t truly understand what happened in their business. Or their hindsight is on a long lag — 30–45 days after month close. If you don’t fully understand the past — there is no reason to contemplate machine learning or AI even though those are the hot buzzwords of the day. Are you getting a true picture of what happened in your business? If not, start by building a system where you can consistently and routinely see what has happened with your most important metrics over time. 

Insight — Why? To answer why is the next step. Figuring out why something happened takes time and prioritization — two of the most precious resources of a business. But with sufficient time spent and prioritization of understanding why, a company can generate significant value from their data. This is where we see the lowest hanging fruit for businesses in the middle market. As we stated above, the tech solutions that are out there make the insight step less daunting from a resource perspective. Companies can easily jump start their understanding of “why” by dropping in a couple of data tools, and creating real-time dashboards. The biggest challenge is how do companies consistently and routinely understand why and make a truly data-informed ecosystem. 

Foresight. What will happen? How can we make it happen? Once companies have all the data tools in place, they can now make really interesting and game-changing decisions from their data. Predicting what happens in a particular part of their business, executives can drive decisions that are best for their shareholders. This is where many people start using words like “machine learning” and “artificial intelligence”. Although appropriate at this step, we believe that a combination of real-time data and personnel who are trained to understand data can make these predictions and decisions without the significant investment in a machine learning model or AI. 

Wherever you lie on the data analytics life cycle. The most important aspect is that a company get started with exploring their data. There is too much opportunity cost to not. 

Anthony Wong
Anthony is the Managing Director of Business Intelligence for NuView Analytics

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