Businesses are increasingly reliant on data analytics to track progress, build strategies, improve strategic decision making, and really you could apply and benefit from data analytics on any aspect of your organization. However, the success in applying data analytics depends on the ability of data analysts to transform raw data into valuable insights.
Here are some of the best practices we’ve found to be effective when dealing with data analytics.

Identifying Core Problems

Each data analytics project must be carried out with the ultimate goal of solving a specific problem that is affecting your business’ performance. Therefore, the key problems must be identified during the early stages of planning. Try asking yourself and your team questions like:

  • Is the business hoping to increase conversions by leveraging historical data?
  • Does the organization hope to gain insights into the technological trend it needs to adopt from industry statistics?

Regardless of the goals, you’ll need to have a well-defined problem. Else, you’ll risk wasting precious time sifting through data aimlessly.

Defining The Right Metrics

Once you’ve established the end goals, you’ll need to determine the right metrics that will aid the process. If you’re trying to determine the best marketing channel, you’ll need metrics like traffic, conversions, and engagements.

Sometimes, data analysts put themselves into difficult situations by attempting to interpret a large chunk of data, where most of the numbers do not give insight into the desired outcome. The right metrics are usually numbers that reflect the historical growth and are useful in charting the next course of action.

Balanced Sources Of Data

Often, data analysts turned to internal data sources like operating costs, monthly revenue, and employee demographics to build analytical models. While these data are good indicators in building an analytical model, analysts should balance internal data with external data.

External data sources point to data that are collected by 3rd parties and can be leveraged for decision making in businesses. It’s important for analysts to have the big picture in mind, mainly how the business is interconnected in the supply chain ecosystem.

Engage The Right Data Model

Now that you’ve had the data in place, you’ll need to employ the right model to derive the hidden insights within the numbers. For example, logistic regression is a popular approach for predictive analysis by taking in various parameters to produce a binary output.

If you’re trying to pinpoint business issues, you’ll need a diagnostic model approach. This model requires combing through the data to identify anomalies, which are unnatural figures hinting how the faults occur in the process.

Use Proper Visualization Tools

Your task as a data analyst goes beyond making sense of the chunks of data. At the end of the day, you’ll need to share the outcome with the respective stakeholders. Therefore, it’s always wise to invest in intuitive visualization tools, where the brightly-coloured graphs convey the complexity of data easily to typical individuals.

Anticipating Data Handling Capacity

Even if you’ve got all the basics right, you’ll need to be prepared for the increasing volume of data in today’s business. The term ‘big data’ is associated with data analytics, where the growth of the data volume and velocity has been exponential.

Conventional database systems may struggle to handle the influx of real-time data processing. To ensure you’re well prepared for big data, you’ll need to adopt a data streaming infrastructure as part of the analytic toolsets.

Conclusion

In this article, we’ve outlined some of the best practices you can adopt in data analytics. But if that isn’t your thing, contact Axual and our team of expert will be able to help you leverage the data you have in making the right business choices to stay ahead of the game.

 

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