Building a Data-Driven Organization with BI and Analytics

BI is just a beginning. In order for organizations to truly achieve high ROI, BI needs to feed Analytics to help drive decisions that drive revenue, cost and process improvement.

In a previous blog post, I asked the question are we turning data into knowledge.  In that post, I highlighted the experience the City of Cincinnati Public Schools went through where data was not turning into knowledge, until the faculty and leaders actively understood and used the data.  I focused on cognitive disfluency as a potential catalyst in helping understand and use data, but from a larger picture, what was initially missing was the Analytics.

Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight (BEGD) is an excellent resource for understanding, evangelizing and implementing Analytics in an organization.  The authors drive the point that business analytics doesn’t have to be rocket science.  They believe in the 80/20 rule, where 80 percent of the ROI from analytics can be derived from simple analytics that most people can implement and 20 percent may require the more advanced “rocket science” or predictive analysis.

Nucleus Research found a Return of $ 13.01 for each $1 Invested in Analytics based on a study of companies in 2014. That’s a great return, and reason enough for most companies to make some level of investment into analytic research.

What is Analytics?

BEGD defines analytics as the science of applying a structured method to solve a business problem using data and analysis to drive impact.

Intuition + Data = Actionable insights → Good decision

From Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight

Analytics is not Business Intelligence.  Where BI leaves off is where Analytics begin. The ROI on BI is very dependent on Analytics.  Many companies focus on the top half of the diagram below, driven by IT organizations, but fall short on the bottom half which is driven by leadership across the company.

From Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight 

The Two Sides of Business Analytics

Successful Analytics = Data Science + Decision Science

  • Data Science – The technical track that derives insights from the data.
  • Decision Science – The business track that aligns stakeholders so that valuable insights produced using data science can be inserted into the organizations decision-making process and converted into action.

Just like many other aspects of technology, Analytics requires the technical skills but also requires the soft skills to understand the business, understand the questions that need to be answered, and present and sometimes sell insights that will drive business impact.

4 Keys To Building An Analytics Based Organization

An organization that wants to become a data driven one, using Business Analytics, needs to build 4 areas that define its Analytics Maturity:

1. Leadership

A data driven company, most likely, has data driven leaders that are committed to learning about their customers and all that affect the customers to drive growth and innovation.  They would also believe that data will be a key to drive their business in all areas.

  • As data is used to gain insight to drive a business, it’s important to know the drivers and eventually all the drivers should follow a single vision that the leaders and the organization move towards.
  • Data-driven leaders will be committed to consistently use data to make key decisions in proving or disproving hypothesis, or uncover new opportunities or gaps.
  • Data-driven leaders know that you can only manage what you can measure.  They will use data to keep themselves and their teams accountable to the goals and vision of the organization.

2. Analytics Talent

McKinsey & Company has warned about a potential 1.5 million shortage in the US, of managers and analyst with needed Analytics skills by 2018.  Whether or not this actually happens is debatable, but what’s not worth debating is the significant growth in the need for Analytical skills.  If ROIs that are 13 fold continue, then it’s a no brainer to turn any organization into a data-driven one, with a strong Analytics based business and technical team.

It’s important to keep in mind that Data-Driven is still people driven rather than technology driven.  The ROI is not as dependent on having the most expensive set of tools as much as it is on having the people that can take data and turn them into strong decisions, even if it’s using simple but highly effective tools such as Excel and combining it with an effective organizational decision making process that can implement the insight.

Secondly, it’s important to keep in mind that Analytics skills require both technical skills and soft skills.  There is a tendency to focus more, or sometimes too exclusively, on the technical skills.  A study by Accenture found that US Workers were under pressure to add skills and 52% added technical skills but much less added the soft skills: problem solving, analytical and managerial.

Business side professionals need to develop three key skills:

  • Hands-On Business Analytics and Testing – basic understanding of simple methodologies, use Excel for charting and analyzing and have the basic understanding of A/B testing (hypothesis testing of two variants).
  • Working with Analysts – working effectively with data analysts to support the hands-on efforts for the 80% and partnering with Analysts for the 20% advanced analytics.
  • Introductory BI Advanced Analytics – a overall understanding of the tools such as regression, decision tree, segmentation, etc., to effectively engage with the data analysts.

Data side professionals need to develop four key skills:

  • Hands-On Business Analytics and Testing
  • SQL Skills – pulling and collating data from multiple sources.
  • Hands-On Advanced Analytics
  • Stat Tools – to perform advanced statistical analysis such as SAS, R, or SPSS.

3. Decision Making

Somewhat like a play within a play, effective decision making through data requires effective decision making to get there.  This transparent decision making process should make clear:

  • Kinds of Analytics projects that get funded
  • Criteria for selecting one project over another (financial and non financial measures)

and the process should include these steps:

  • A process for collecting all the proposed initiatives with information about investment and returns as well as how it relates to company vision, goals and/or strategy.
  • A team that will review on a regular basis on both new initiatives and results from previous initiatives.
  • A set of criteria to prioritize projects.
  • A clear description of how the project will be executed and the metrics that will be used to measure success

4. Data Maturity

The post has focused on Analytics but, in order to analyze, one has to have collected and stored the data for easy access and usage.  In addition, the data has to be good data.  One common reason Analytics is missing in some organizations is because it takes so much time and effort to collect, store and prepare the data for analysis.  In some cases that investment in time is not realistic and certainly does not lead to 13 fold ROIs.  The qualities of an effective data infrastructure are:

  • open, flexible and secure architecture
  • scale to handle more data and users
  • expand to handle more and different types of data
  • delivers performance to handle complex and large volumes of data
  • it can interact with a changing technologies and tools
  • easily accessible by authorized business and data analyst users

Key Take Aways

  • State of the art BI on its own just gives you data most of the time.  Turning that data into Insight and then into Action that impacts the business is through Analytics.
  • BI feeds Analytics
  • Effective use of Analytics has yielded ROIs of 1300%
  • Analytics involves both technical and soft skills.  After gaining insight, implementing into action and decisions require strong understanding of the business, interacting with leaders and stakeholders, selling the action plan and managing to completion and then measuring results.
  • Analytics is a combination of data science and decision science

I highly recommend the the book, BEGD, as a great starting point for implementing Analytics or if you have an opportunity to improve an existing Analytics solution for your company.