Deciding on the best approach to data analysis

The approach to data analysis determines the quality of findings and insight. While technology and expertise are important, there are only two-thirds of the equation. The biggest determining factor is the approach. How do organisations use technology and expertise to accomplish business objectives? Putting together the right strategic initiatives can make the biggest difference for both small and big businesses. Failure to plan properly hurts businesses because they could spend huge sums with little ROI.

A strategic approach to data analysis

Setting the right objectives and curbing aspirations

What do we mean by a strategic approach to data analysis? It means setting the right business objectives so that selected technical solutions take the team one step closer to accomplishing these goals. Several companies invest in data analytics platforms (and spend thousands of dollars) to completely transform the way their business works.

In such cases, organisations always end up falling short because analytics cannot live up to these transformative expectations. Unfortunately, data analytics was not developed to make sweeping changes across an organisation overnight. What it is built to do is solve specific problems and accomplish objectives using the data generated by the organisation.

To resolve the situation, business leaders and data teams need to agree upon a common objective.

Once this objective is set, data teams can optimise data environments and analytics to generate the necessary insights. Focusing on a specific objective is an important strategic approach to data analysis because it brings data teams and business people together in mutual understanding, instead of leaving them on opposite ends of the understanding spectrum (an all too common occurrence in business).

Furthermore, it gives data analysts a solid idea of what business leaders are looking for, making it easier to map out the data analysis process and ensure each step takes the team one step closer to completing business aims. Data teams have an easier time deciding what to measure, selecting the right measuring techniques and analysis methods. It also becomes easier to achieve the transformative aspirations of the upper echelons of leadership over time.

Maintaining a balance between centralised and decentralised work

The best approach to data analysis is maintaining a balance between centralised and decentralised practices. Over-centralising hurts productivity and efficiency, but no centralisation hurts focus. Maintaining a balance between independence and accountability can be challenging, but there are practices to aid, in this regard.

Automation tools can help with data analysis by removing mundane, unnecessary tasks from the data team’s plate, giving them the independence to be more productive. Documentation is an excellent example because data scientists need to document their work to better understand work processes. However, it is a time-consuming task, especially when the engineer has to document every new table or idea.

Automated systems make the entire process easier by creating documents on behalf of the data scientist. Data analysts still need to state the specifics of the new tables, but a large part of the documentation process is out of their hands. This means less time spent in bureaucracy and more time spent on idea generation.

Another option is to set up centralised libraries for common metrics. Be it healthcare or finance, there will be common metrics across several industries. However, there is a problem when data scientists teams create a different methodology to measure the same number.

This not only creates needless work and duplication of processes, it hurts decision-making because different methods can generate different results for the same metric. Data scientists will benefit tremendously from the use of these libraries because they can pull most of their metrics from already established libraries, while still adding additional metrics based on the client’s needs.

Charting the best approach to data analysis

Choosing the best approach to data analysis is like chartering a course on a ship. The captain (business leader) can have a compass (tools) and the right crew (data science team), but all of it is for nought without the right systems and objectives in place. Hence, business leaders need to get the process right. The right process can make the difference between timely ROI and no results after years of spending. Hence, it’s important to consider the best approach to data analysis.