Why every business needs a data and analytics strategy?

MX Bites / November 2, 2022

While most companies recognize that their data is a strategic asset, many are not taking full advantage of it to get ahead in the crowded market. In this write-up, we discuss the key elements of a successful data strategy that help businesses make informed decisions. 

Even as companies make larger investments in data and analytics initiatives than ever before, age-old obstacles like siloed and untrustworthy data, inefficient data management practices, and a lack of meaningful insights continue to get in the way of data-focused analytics initiatives. 

Many business leaders educated in the 80s refuse to get a divorce from good old excel and legacy solution providers with “branded hype” that never evolved with time. While divorces are ugly, sometimes they are sine qua non and no therapy can help. 

What is an Analytics Strategy? 

An analytics strategy is part of a comprehensive strategic vision to specify how data is collected, processed, and used to inform business decisions. It is meant to provide clarity by:

• Specifying the sources and types of data that are collected and used for reporting
• Guiding key decision-makers on how to evaluate and digest data for better decision
• Enabling business stakeholders to develop the necessary capabilities to answer questions, influence operations, and improve real-time decision making

Analytics strategy should translate the data strategy into an actionable plan to implement it. It should address organizational objectives, and desired business outcomes from data, educate stakeholders and establish a plan for implementing the strategy. 

How to create an Analytics Strategy? 

To create an analytics strategy, let’s review some steps we recommend:

Step 1 – People: Identify Key Stakeholders 

The first step in formulating anything should be to identify your key players. These folks should have a vested interest to make organizations more data-driven. 

Key people should be cross-functional to ensure that different interests of the organization are there to give input. Some examples of primary stakeholders include:

• Business Leaders – Individual leaders in this role help contribute and align corporate strategy and vision with the analytics strategy.
• Data Consumers – These users will help provide insights into how teams will use the data within the business, as well as how the data is being used today
• Project Management – This role will help coordinate the cross-functional effort to ensure deliverables, implementation, and help escape the “too difficult” mantra.  

Step 2 – Processes: Perform Initial Discovery 

The second step in developing an analytics strategy is to conduct several discovery sessions to uncover the current processes around data analysis in your business if any. 

Some common questions to ask in discovery include:

• How do you use/analyze your data today? 
• What do you use to answer questions with data? 
• What questions are asked that you cannot answer?
• What manual and repetitive tasks are done? 
• What does the current data collection look like?
• Right data vs good data, which one do you collect? 
• What source systems are/will be part of the strategy? 

You’ll notice the first four questions are more geared towards your business users, while the last three focus on the systems and data that are going to get us what we need.

Step 3 – Technology: Select Tools of the Trade

Since we have already identified the core objectives of our business units and desired outcomes of our analytics initiatives, this part should be relatively straightforward. 

Here are a few factors to consider when choosing an analytics platform or partner: 

• Cost – The pricing model is one thing, but how much time will it take to upskill, deploy,  manage, and maintain the solution once it’s been purchased?
• User Interface and Visuals – Self-service analytics tools should be user-friendly and easy to understand regardless of the user’s technical background.
• Advanced Analytics –  While it may not be a near-term goal, a future goal of the analytics program should be to begin to move into predictive and prescriptive analytics.
• Collaboration – Your analytics tool needs to allow users to share, analyze, and interact with data in different locations to enable more collaborative decision-making.
• Security & Privacy – Can it “inherit” security settings from underlying systems? Can you control which groups of users have access to which levels of data?

Step 4 – Culture: Establishing a Data-Literate Culture 

Your key people need to understand the basics of working with data: structured vs unstructured, how to extract the best insights, and how many manual processes could be automated. 

Here are a few points to focus on to create a data culture: 

• Lead by Example – Data culture has to start from the top. Leaders demonstrating data-driven decision-making is the first crucial step to fostering a data culture. 
• Measure What Matters – Leaders also need to be intentional with which measures and metrics they choose to report or base decisions on. Less is more. 
• Make Data Accessible – The best way to get key people what they need is to start with high-level, aggregated data and work your way into drilling down more in-depth data. 

Analytics strategy like every other is an ever-evolving process. Just because you have created the 4 steps outlined above, doesn’t mean you are done. It will continue as a malleable ongoing business effort.