September 2020: Big Data & Small Organizations

BIG DATA & SMALL ORGANIZATIONS – Learn to use data as a decision making tool

By: Scott Warren and David Nelms

“Big data” isn’t just for big companies. Many organizations feel they are too small for “big data,” but data size is relevant to the size and complexity of the organization. All organizations have a preponderance of data that can help them make better decisions. Actively and consistently using actual data to inform allows organizations to track results and outcomes of decisions and, ultimately, can point decision-makers in the direction needed to identify problems or opportunities.

This newsletter reviews the necessary steps an organization should follow to enhance data as a decision-making tool. In addition, it will expand on two of the steps, data governance and culture. Both are critical to success but often overlooked.

Data-based Decision Making – An Example

Data-based decision making isn’t as scary as it sounds. It’s really just the process of making operating decisions based on data rather than intuition or observation alone.

Financial statements are a common example of using data to make decisions, but leveraging additional operating metrics can help decision making to improve financial results. Here is an example of when Warren Whitney used operational data for a client. This client, a ski resort, had created a data warehouse that recorded data from five of their major operating applications. This data warehouse allowed us to pull statistics on food consumption and the types of skiers (daily ticket holders vs. season ticket holders). The figures demonstrated a strong correlation between the type of ticket and the amount of food sold. This information on the number of skiers by type of ticket holder was sent to the chef daily to provide information to help with lunch preparation. It helped him prepare accordingly to reduce waste and increase profits.

Basic Steps

Data should be at the core of strategic decisions. Data provides insight to determine the best path forward for an improved business model. Here are essential steps to take when implementing data-based decision making.

Data Acquisition:

Make sure your data strategy is aligned with your business objectives.

  • Clarify and clearly define the strategic objective – start by identifying the questions you are trying to answer
  • Research the technology needed to collect the data
  • Determine the supportive data points
  • Evaluate the difficulty in collecting the data points compared to their importance – not all data is created equal.

Your data should be more than financial. Include key performance indicators (KPIs) from all major areas (e.g., operations, customer service, marketing, and sales, finance, etc.) to help ensure that decisions will be balanced. Don’t expect to design a perfect approach the first time. Get the basics right, and then evaluate and improve.

Data Governance:

Manage the availability, usability, integrity, and security of the data. This is called data governance; it is a critical component of the implementation (garbage in, garbage out). Ensure that you have:

  • Close alignment with business goals
  • True accountability
  • Data use guidelines that are enforced by someone who takes ownership of the process
  • Active risk assessment
  • Functional data literacy for all teams and business units, including a common understanding of definitions of the types of data you are collecting.

Data governance is crucial because it ensures that the data is consistent and reliable. An example of the importance of uniform data collecting is in retail. Retail often measures “sales per square foot.” “Sales per square foot” allows retail space to be analyzed for many of the operating decisions. Still, this simple data point can be full of inconsistencies from store to store unless all the components are clearly set out. For example, what is included in “sales” – returns, coupons, over/under transactions, etc.? Does square footage only include the sales floor, or does it include the backroom, changing rooms, and office? If these elements are not specified, the data will be inconsistent. In addition to consistent data collection guidelines, the data should be audited periodically for accuracy.

 Data-based Decision Making Culture:

Create a plan to ensure that foundational data literacy becomes a part of the corporate culture. To be effective over time, you must have:

  • Standardized data
  • A repeatable process
  • A shared understanding of how the data is used to make decisions.
  • A regular evaluation of the process to see what needs to be changed.

You don’t have to be a data scientist to benefit from data decision-making, but it needs to become part of your company culture. Management and staff must understand how and why data is critical to success. Teach your team how to organize data, use visualization tools, and understand the difference between correlation and causation. Also, explain to your managers that their recommendations will be tested by data.

The company culture will evolve from “what do we think” to “what do we know.” Use data to make a decision, not to justify one. Decision making will become a cross-functional cooperation – right people, right data, problem-solving capabilities, and process.

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If you have any questions or seek further clarification on the data-driven decision-making process, please call us at 804.282.9566. The professionals at Warren Whitney are grateful for the opportunity to support you and your business. Our fractional assistance and project work can help you think through decisions and execute the strategies. In addition to supporting “big data” we can put together cash flow projections, manage HR issues, adapt technology and processes, and devise a strategic plan. We Make Potential Happen.