4 steps to building a data-driven organisation

Technology and the burgeoning digital skills gap once again dominated discussion at the recent World Economic Forum in Davos. One lively panel session, ‘Making Digital Globalisation Inclusive‘, saw the CEOs of Microsoft, HCL, Dell and Salesforce in agreement that the world is facing a potentially dangerous digital divide.

One of the main issues cited among participants was the emerging gap between the data capabilities of the private sector, which are vast and increasing exponentially, and the absence of those capabilities in the public, civic and non-profit industries. However, the evidence suggests data capability is a sector agnostic issue.

Most organisations, public and private, are merely scratching the surface when it comes to harnessing their data; research from IDC suggests up to 90% of data remains unprocessed (or unstructured) and unanalysed. One of the underlying issues is that with information being generated by every department, individuals are often unaware of whether it is their responsibility, or even if they are allowed to utilise data or not. To get the most value out of data all employees need to have the capability to analyse and draw conclusions from it. Effective data analysis must not remain the sole dominion of a few, highly qualified experts.

So, how can organisations make the most of the information which is now at their fingertips?

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Start from solid foundations

Organisations must ensure that their data is fit for analysis by multiple parties. There are huge caches of data being produced today that need to be accessed by a diverse range of people. Without the necessary technology in place to prepare that data for analysis it becomes harder to make sense of what the data is showing.

Data curation tools and processes (like data catalogues and semantic governance) are now converging with BI platforms to link data with its business context and maintain governance at scale. This helps analysts and content consumers who need to verify data origins through lineage analysis, as well as data engineers and data stewards who look at the downstream impact of changes to data sets. Ultimately, governed data curation will provide a stronger foundation for the entire analytical pipeline.

Make data part of the everyday skill-set

Almost every job function in every sector is now touched in some way by data. Granted, certain aspects of AI and data science require specialised technical skills, but the reality is that everyone from the HR department through to the boardroom needs to be able to read, understand and communicate data as information.

The signs are that organisations are taking this challenge seriously through investment in data literacy training and a focus on apprenticeships to broaden the talent pool. Real and long-lasting change is going to require sustained action and tangible investment from both the government and the private sector.

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Make it easier to ask smart questions

We’ve all witnessed the pained expression that creeps across the face of an employee when they’re forced to grapple with a complex piece of technology. When systems are hard to use, most employees simply don’t use them. As organisations expect more people to become comfortable using data as part of their everyday role, the technology itself must be adaptable to a variety of different skill-sets.

As James Eiloart from Tableau predicts in Medium: ‘Natural language processing (NLP) helps computers understand the meaning of human language. BI vendors are incorporating natural language into their platforms, offering a natural language interface to visualisations. Successful systems can convert nuanced language and colloquialisms, seamlessly converting those into queries. A statement can be developed and iterated on, just like a conversation.

‘When people can interact with a visualisation as they would a person, it allows more people of all skill sets to ask deeper questions of their data. As natural language evolves within the BI industry, it will break down barriers to analytics adoption and help transform workplaces into data-driven, self-service operations.’

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Build a culture of storytelling

According to Anand Ekambaram in ETCIO.com, ‘If you can’t communicate data findings, you can’t make an impact with your analysis’. This is the power of data visualisation. It’s a critical skill for analysts to be able to convey the steps in their analysis that led to insights in an actionable, easy-to-understand way, also defined as “data storytelling”. As companies create a culture of analytics, the definition of data storytelling is changing.

Instead of presenting a singular conclusion, today’s data storytelling methods emphasise nurturing a conversation. This crowd-sourced approach to analytics puts responsibility on both the dashboard creator and the audience to come to a conclusion around what the data tells them. This invites a diversity of perspectives before making a business decision. Embracing data storytelling across jobs will amplify the potential for business impact as data is used to engage, inform, and test ideas enterprise-wide.

Taking these aforementioned steps will help any business build a data-driven culture, but from our experience, true success will only come from the marriage of excellent technology with deep cultural change. Encouraging more people to use analytic tools is partly down to the technology, as well as helping everyone feel comfortable using data in their jobs. Once more businesses realise this, they will see far more value being generated from their data insights.

Written by Andy Cotgreave, senior director at Tableau Software
Written by Andy Cotgreave, senior director at Tableau Software

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