Businesses are feeling tremendous pressure to ensure their organisation is data-driven in order to remain competitive. Yet C-Suite surveys show there is still quite a bit of confusion that remains about how to implement an effective data culture. According to a recent report by KPMG Capital, 96 percent of enterprise companies surveyed admitted they could do more with big data and make better use of analytics in their organisation. So why are the vast majority of large enterprises today struggling with how to turn data into insights, and insights into a competitive advantage?
Most organisations today are struggling with how to ‘connect the dots’ because they don’t have the right infrastructure in place to create a data-driven enterprise, and for many, determining how to integrate the technology needed to become so is problematic. Add to this the fact that year over year IT budgets remain flat (plus or minus ten percent), and that every CIO spends 70-80 percent of their cost structure and IT budget on ‘keeping the lights on’, or maintaining business as usual, which doesn’t leave much for new IT innovation . (Source: Tech Pro Research (TPR) conducted a survey in August 2015 — IT Budgets: Drivers, trends and concerns in 2016.)
In the race to embrace big data, many companies would instinctively move would to hire more data scientists and charge them with making sense of the mayhem. But there are a couple of big problems with this approach: First, there is a big shortage of data science skills in the market, and second, there’s a notable difference between data science and data-driven decision making. Additionally, creating or expanding a data science team might not necessarily lead to more widespread use of enterprise information to derive the business value. In fact, by building out a ‘specialized data scientist team,’ companies might simply exacerbate the organisational gap that exists within enterprises today – wherein IT departments enable only data scientists with data access because those citizens know how to get and integrate data. In doing so however, they inadvertently create shadow IT because everyday business workers subsequently resort to using non-sanctioned tools and apps to access the data they need to be effective.
Self-service data preparation is certainly part of the answer to this challenge. Historically, the task of data preparation has too often fallen solely to overburdened IT teams that can’t keep up with the growing data demands of the business, and data analysts who are all too frequently spending more time wrangling data than they are supplying insights. Fortunately, with the advent of the latest open source-based, self-service applications, all of this is now starting to change.
One of the great advances delivered by self-service data preparation is that it extends the benefits of digital transformation to all business users versus. solely a few data scientists and IT experts. The ability to explore, cleanse, enrich, and combine data in minutes instead of hours allows line-of-business users to apply their own unique domain expertise and work directly with the data relevant to their business objectives.
While enabling ‘data access for all’ is certainly a way to derive greater value out of enterprise information, it’s critically important that this process be carefully managed and governed. Self-service should not just empower line-of-business workers in isolation. IT has an important role to play in terms of ensuring that each data-empowered employee has a way to share and collaborate on data insights with other members of the organization. Most of today’s data preparation solutions only allow a specific group of data-savvy users to access information stores—thus IT should seek out more advanced solutions that move beyond the short-range capabilities of today’s point solutions by enabling true collaboration between IT and business workers. In doing so, employees become ‘information brokers’ with the freedom to prepare and analyze data themselves, helping speed time to insight. At the same time, IT is able to maintain data security and governance.
Stakeholders that are in charge of provisioning, managing the quality and securing of data assets, such as data architects, IT developers or data stewards can take handle of data governance, data control, and reuse. They can deliver sanctioned data as an on-demand data set, exposing the right data to the right people at the right time. And, they can as well crowdsource data preparations rules designed by the business user who best knows their data, operationalise those rules and publish them across the organisation. This is truly an example of data-driven business in action—i.e. a process elastic enough to empower more business workers with access to analyse data and become more enlightened and effective in their day-to-day roles, while simultaneously enabling IT to maintain both data governance and compliance.
With the help of the right technologies companies can become more nimble, informed and competitive. By breaking down the barriers between enterprise data and the people accessing that data, IT can bypass the skills gap and foster continuous innovation. Through governed, self-service data access, IT can empower everyone in the organisation to turn their daily tasks into data-driven activities, which results in a smarter, more successful company. Business users are happy because they have the freedom to prepare and analyse data themselves, expediting time to insight, while IT is able to maintain data security and governance.