The act of preparing data for analysis eats up significant resources, with IDC estimating that analysts typically spend at least 75 percent of their time on this task alone. This time could be put to better use on more strategic tasks, particularly at midsize organizations with fewer dedicated data science team members. 

That’s why it’s so critical that these companies automate data analysis. When done right, this drives growth and opens up new opportunities for innovation, including harnessing AI for greater efficiencies and cost savings. One caveat, however, is that midsize data is often messy—contained in spreadsheets and plain-text files of varying formats, making integration a challenge for the automation process.. 

A recent HBR article offered several tips to overcome this and other potential obstacles, which we’ve summarized below, to help you get started on automating data analysis, including: 

Prioritize Cleanup

Midsize organizations don’t have the resources or bandwidth to tackle every opportunity, so they should focus automation efforts on areas where critical operations are either inefficient or too dependent on a small group of people. From there, it’s important to identify the project’s relevant data and scrub it to ensure it is useful and delivers accurate results. 

Hire the Right People 

Another critical consideration is staffing. Data leaders generally don’t have the time to add automation responsibilities to their plate, and hiring a new point of view can often drive benefits for the business. For example, the HBR article included a case study of one midsized organization that hired an intern from a master’s program. Armed with fresh insight, she identified ways to turn analytical processes into algorithms—freeing up employee time, increasing revenue opportunities, and driving organizational efficiency. 

Look to the Future 

After companies have thoroughly prepared their data and hired or trained staff as needed, they can begin thinking about AI. In order to be effective, AI and ML need to train on large datasets with both confirmed positive and negative outcomes. Once a midsize organization has completed its data cleansing and conducted a few algorithm-based sweeps, this large dataset should exist and be able to train an AI model.

For a more detailed guide to the steps involved in implementing automated data analysis in a midsize company, read the full HBR article.