Data workers and the necessity for the self-service analytics revolution

This is a guest blogpost by George Mathew, president & COO, Alteryx

In recent years, we’ve seen a trend line for data volumes and velocity to go up, and to the right. This continued growth has been further fueled by the Internet of Things (IoT) and wearables. As a result, companies are wrestling with more data than the people, processes, and technology to drive analytic insight from this data deluge.

During the past decade, data scientists have been employed to manage corporate data lakes, muster up unique analyses, and extract valuable insights to be shared for a broader set of analytic consumption. However, plagued by a never-ending data deluge, it’s becoming increasingly obvious that data science will not solve all the world’s analytic challenges. There are simply not enough data scientists to go around.

An alternative approach is to spread data throughout the organisation, putting information into the hands of “line-of-business” users rather than keeping it locked in technical siloes. These data workers have been using Microsoft Excel as their ‘go-to’ analytics tool for years. However, as more and more data sources have emerged in the last half decade, we’re seeing a need for Excel to have ‘a best friend’ of sorts, who can power through this data deluge. Data workers are looking for more self-service tools that enable businesses to make the most of their data.

Self-service analytics equips the individuals who make business decisions, by enabling them to dissect and analyse data problems themselves, in more depth than ever, without being a programmer at heart. The result? Deeper insights, in hours rather than weeks. And as data workers are more familiar with their own functional domains, they become far more effective at working on various projects at one time.    

Despite better tools to enable data workers, the culture around data remains an obstacle within most businesses. In organisations practicing a centralised model of data analysis, there’s still strong reticence to allowing data workers to dig into their corporate data. Today’s organisations need to foster a more open, creative policy towards data consumption, rather than restricting access. Data workers need to have personal understanding and engagement in order to understand their data, manipulate it, analyse it, and act on it without needing external experts to do this work.

Whilst creativity and openness around data needs to improve, there is still a very recognisable need for proper governance. Increasing volumes of data within large organisations puts billions of dollars at risk, especially as spreadsheets are still a very insecure approach to analytic consumption. Instead, businesses need to find the proper balance between fostering creativity and governing data, ideally with stronger tools that are purpose-built for data workers. These two objectives need not be mutually exclusive – a great environment for creativity around data can also improve security and governance through managed sharing and output.

In this brave new world, there will likely be incidents of data spillage. Rather than living in fear of spillage, companies need to develop an environment that focuses on understanding why it’s happened and how best to recover from it. This conscious effort to drive analytic curiosity and a ‘culture of data’ will only position today’s labour-force of data workers to higher levels of achievement. The expansion of the self-service analytics market will prove to be a game-changer in unleashing the potential of today’s data worker, thus completely changing the business landscape as we know it.

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