Insights and Outcomes - Data Analytics Simplified

My daughter once saw one of my spreadsheets, shrunk-down and stretching across two monitors.  Her reaction?  “Dad, I have no idea what that is, but it looks awful and I never want to do it.”  What she saw was the creation of new information that drove better decisions, increased sales and generated profits.  What she saw was Data Analytics.

Too often people’s eyes glaze at large amounts of data.  Combine “data” with “analysis”, many tune out because, let’s face it, they’re not wired to like work with large numbers of numbers!  And they don’t understand.  

Let’s simplify Data Analytics.  At its heart, it is a discovery process applied to data that creates new insights and potential outcomes.  Let’s take a peek at those definitions to see how they relate.

  • Insight – the capacity to gain an accurate and deep intuitive understanding of a person or thing

  • Outcome – Something that follows as a result or consequence.

Data Analytics transforms a company’s data into new insights.  It creates better information and recommendations that lead to improved performance.  

How Data Analytics Leads to Better Outcomes.

To some degree, every business uses data analytics, even if they don’t call it that.  Building on what’s there, broader Data Analytics capability leads to better outcomes in a number of ways:

  • Analysis is efficiently repeatable – That really cool special project that took 3 weeks to complete really WOWED! the team. It can readily be updated and expanded.

  • Business Analysis becomes timelier – Leaders want monthly results sooner, so accounting is always challenged to close the books earlier. Analytics gives you the capability to deeply explore why you got the sales and profits you did. Faster.

  • Data is connected across functions – Marketing looks at share, customer information, product sales. Sales looks customers, products. Finance looks primarily at sales and profit dollars. Data Analytics connects all the dots.

  • Information is easier to understand – Done well, analytics turns numbers into pictures that are much easier to understand.

    Once you start using data, it’s hard to imagine how you made the decisions you did without it!  If you begin thinking like that, you’re on your way to creating a data driven culture. 

What’s Different a Data Driven Culture?

The traditional company business review tells WHERE ARE WE?, usually compared to a budget or prior year results.  Each function discusses results based on what they “know”.  Analytics answers WHY?, with insights based on facts, not intuition or a leader’s tummy.  An experienced Data Analyst uses the insights to recommend WHAT TO DO? to improve outcomes.

After all, every business wants better outcomes.  More Sales.  Productivity.  And, yes, Profits.  

Building a data driven culture is rarely a stated company goal.  It happens organically, especially in the early stages.  Typically starting in finance, someone begins to pull a lot of data into a big excel workbook.   Or someone is finally given a project to look into a specific problem.

 IT eventually gets involved with on a reporting project basis.   Inevitably, more information requests need more reports, more reports lead to more information requests.  Momentum builds from there, sometimes rather haphazardly.  

Eventually enough data is built into the strategic plan.  KPI’s become the focus of performance management processes.  More information gets reported throughout the company.  However, even in this scenario, discussion seems to stop with “where are we”?

Challenges to Building Data Analytics Capability

Data Analytics.  Big Data.  Business Intelligence.  Artificial Intelligence.  All of these have been around for years.  Story after story highlights how companies deeply analyze data to understand broad market trends, customers and consumers, operational efficiencies, quality, costs, etc.

So why haven’t so many companies, of all sizes, succeeded in creating cross-functional Data Analytics capability?  From my experience, they’re missing some fundamental building blocks:

  • Senior Cross-Functional Data Champion – a decision maker who recognizes the value of understanding how measurable activities across company connect and drive performance.

  • Efficient Access to “decent” Data – the processes to download, cleanse, stage and analyze data can be overwhelming in the beginning. Even simple tools can help make analysis repeatable.

  • Dedicated Resource – it has to be a priority for someone in business and IT. If left to the last 10-15% of someone’s time, progress will be limited.

  • Data Creator/Consumer Experience – An experienced Analyst with extensive business leadership can identify the nuggets of value among all of the interesting trivia analytics can create.

Putting these in place creates the “will” and foundation to start the Data Analytics journey.  Analytics is not something that happens quickly.   There will be challenges in changing work processes, technology and company culture.  Data2Profit can help be your guide.