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Developing a Data Analytics Strategy

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data analytics strategy

While data-driven digital platforms and the recent pandemic have opened new and novel opportunities for businesses to go beyond geographical borders and compete in truly global business environments, an average business faces tremendous pressure to demonstrate the value of their operations. With the rising importance of data as the new oil of a global business environment, data analytics strategy has become a core component of most business operations. 

Indeed, according to data industry expert and thought leader Bernard Marr, the International Institute for Analytics predicted that “by 2020, businesses using data would see $430 billion in productivity benefits over competitors who are not using data.” 

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However, many businesses still do not have a cohesive strategy for implementing data analytics in their organizations. The effectiveness of enterprise data analytics functions can be measured via “outcomes,” for which businesses need to develop a plan known as a data analytics strategy. Without this strategy, business goals remain unclear and data analytics efforts can go wasted. 

Data Analytics Strategy: A Definition 

With the frenetic adoption rate of the “data-first” approach to business operations, today’s businesses are making heavy investments in data platforms, technologies, and tools to improve decision-making. Although businesses have been using data to drive business decisions for some time now, they do not always reap the maximum benefits of a data-driven culture, because they have not taken the time to plan and develop a data analytics strategy. 

The data analytics strategy in your enterprise defines the master plan or blueprint for using data for making enhanced business decisions and for measuring performance through key performance indicators (KPIs). To this end, the documented data analytics strategy may contain rules, policies, procedures, roles, and responsibilities related to all data operations within the enterprise. 

The first step in developing an effective strategy is analyzing the current customer base, then comparing the existing customer metrics with those of the competitors’ customer base. This approach helps to define performance objectives for future business decisions and for setting KPIs. Some sample performance objectives may be:

  • Enhanced brand awareness 
  • More variety of products based on inputs from customer research  
  • Increase in sales volume by 10%
  • Increase in website traffic volume by 20%
  • Increase social media presence (followers and engagement) across channels by 5% 

Now, with a list of well-defined objectives, the business can set out to identify the KPIs most important to it, and then actual performance data to measure the defined KPIs against.  

5 Steps Involved in Developing a Data Analytics Strategy

Organizations should typically follow these five steps in developing a data analytics strategy:

  1. Developing the data culture 
  2. Determining key performance indicators (KPIs)
  3. Defining data technology infrastructure: Data Quality objectives  
  4. Researching and developing target markets
  5. Creating the analytics strategy plan

Step 1: Developing the Data Culture 

A data analytics strategy will help the business to set future performance goals through data analysis, desired performance metrics, and measurements. To make business users aware of the benefits of a “data-driven” culture, both high-level (executive) and low-level meetings and presentations can be set up, which demonstrate solid proof of concepts. Developing multimedia presentations shared across a distributed network of business units is easily accomplished in this age of advanced digital technologies.

Step 2: Determining Key Performance Indicators (KPIs)

While analyzing the existing customer base for the purpose of competitive analysis, the business has to:

  • Plan and collect the most relevant data across business units
  • Store the data where it can be easily accessed and retrieved for later use
  • Integrate the collected data (database) to other systems like a CRM, ERP, or marketing database for the defined data analytics strategy to work effectively 
  • Collect competitors’ customer data through various external data channels
  • Set up appropriate data platforms and technologies for comparative analysis
  • Begin customer research and competitive analysis with competitors’ customer data
  • The KPIs should be “specific, measurable, attainable, time-bound (SMART),” with desirable success metrics 

Data analytics helps businesses grow customer bases, control churn rates, monitor product performance, and most importantly, measure customer experience. Thus, data analysis on this step of the strategy will set the blueprint for future product or service improvements or developments for totally brand-new markets. In a business survey, “90% of the marketers and data scientists” agreed that a data analytics strategy is important for business success.  

Step 3: Defining Data Technology Infrastructure: DQ Objectives  

During the execution of this critical step, the business must step back and take stock of internal processes to find the best way to make a number of processes work collectively to convert raw customer data from disparate systems (CRM, FB Business Manager, Google Analytics, marketing automation software) into competitive market intelligence. 

Thus, the fitness of the underlying technology infrastructure will play an important role in ensuring success of this step. The technology infrastructure includes data collection, storage, preparation, and integration technologies because, typically, data will be gathered from disparate sources, stored, and integrated for further analysis. The strength and quality of the hardware and software technologies involved in this step will determine its success or failure. 

This step is critical because the volumes of customer data must be reorganized and streamlined to align with measurable goals defined in Step 2. Once the relevant customer data has been mapped against the SMART goals, it’s time to move forward for the competitive analysis and future (predictive) business decisions. 

Step 4: Researching and Developing Target Markets

This step involves collecting, organizing, synthesizing, and using information about a specific market, collected in step 2, their customers, and the successful market operators (your competitors), for the purpose of penetrating new markets. Review this valuable presentation about turning data into differentiating features.

Step 5: Creating the Analytics Strategy Plan 

The final step is developing the “blueprint” or the master plan for the data analytics strategy. Once all the above steps have been completed successfully, and the defined business goals have been met and measured against actual business performance metrics, the strategy document can be tuned and preserved for future use. 

Although this is called a master plan, it will have to be periodically tweaked and refined, based on new or changing performance parameters or future business goals. For example, Digital Analytics Program (DAP), in partnership with digital.gov, developed a data analytics strategy to track and monitor website performance.

The data analytics strategy guide should serve as a starting point when you stumble across new business problems like gathering information about how viewers are interacting with your company’s website. The specific questions that you need answered may be:

  • Which metrics will provide that information?
  • Are those metrics qualitative or quantitative?
  • Can you accurately measure qualitative metrics?
  • What are your expectations from the measurements?
  • How can you use the results to enhance viewer interactions with your website?

To form the questions and retrieve answers, you will need to consult the data analytics strategy. 

Conclusion

Because data analytics is the backbone of today’s data-first businesses, companies of all shapes and sizes need to plan and develop a solid data analytics strategy to extract maximum benefits from their enterprise data-analytics efforts.

Image used under license from Shutterstock.com

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