Procurement 4 April 2017

Seven data literacy trends small business owners need to be aware of

Data literacy
Business models are changing because of the demands driven by data, and new platforms and technologies are constantly being introduced to help with the change

Here, Dan Sommer, a director at business intelligence software provider Qlik, outlines the emerging data literacy trends that could prove vital to the growth of small companies.

It was reported that up to 80 new companies were born every hour in 2016. That’s a huge number of businesses that need to establish new teams and that need to bring people together who have the most relevant skills and know-how. And to succeed in today’s market, this must go beyond a traditional business skillset.

With so many businesses defined or enabled by the latest technology, it is now an integral part for a UK company of any shape or size to have teams that are actively able to work with – and master – the huge amounts of digital information available, such as data scientists, application developers, and business analysts, if they are to ever be a success.

However, lack of skills continues to be an issue. It is likely there will be fierce competition among businesses to snap up this talent whenever it becomes available, with many smaller business owners potentially priced out of hiring data analysts and the like. .

The situation needs to change. Yes, that will mean upskilling more data scientists in 2017, but there will be a greater focus on empowering employees more broadly.

That will go beyond information activists and towards providing more people with the tools and training to increase data literacy. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.

So, what will change to see culture-wide data literacy become a reality among start ups and growing businesses? Here are my predictions:

  1. Combinations of data

With more fragmentation of data and most of it created externally in the cloud, there will be a cost impact to hoarding data without a clear purpose. That means we’ll move towards a model where businesses have to quickly combine their big data with small data so they can gain insights and context to get value from it as quickly as possible.

  1. Hybrid thinking

Hybrid and multi-environment will emerge as the dominant model, meaning workloads and publishing will happen across cloud and on premise. The cloud provides scalability and ease of access, with long staying power provided by on premise.

  1. Self-service for all

Freemium is the new normal, so 2017 will be the year users have easier access to their analytics. More and more data visualisation tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce.

With more people beginning their analytics journey, data literacy rates will naturally increase — more people will know what they’re looking at and what it means for their business.

  1. Scale-up

Much a result of its own success, user-driven data discovery from two years ago has become today’s enterprise-wide BI. In 2017, this will evolve to replace archaic reporting-first platforms.

As modern BI becomes the new reference architecture, it will open more self-service data analysis to more people. It also puts different requirements on the back end for scale, performance, governance, and security.

  1. Advancing analytics

The focus will shift from “advanced analytics” to “advancing analytics.” Advanced analytics is critical, but the creation of the models, as well as the governance and curation of them, is dependent on highly-skilled experts.

However, many more should be able to benefit from those models once they are created, meaning that they can be brought into self-service tools. In addition, analytics can be advanced by increased intelligence being embedded into software, removing complexity and chaperoning insights.

But the analytical journey shouldn’t be a black box or too prescriptive. There is a lot of hype around “artificial intelligence,” but it will often serve best as an augmentation rather than replacement of human analysis because it’s equally important to keep asking the right questions as it is to provide the answers.

  1. Visualisation as a concept will move from analysis-only to the whole information supply chain

Visualisation will become a strong component in unified hubs that take a visual approach to information asset management, as well as visual self-service data preparation, underpinning the actual visual analysis.

Furthermore, progress will be made in having visualisation as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.

  1. Focus will shift to custom analytic apps and analytics in the app

Everyone won’t – and cannot be – both a producer and a consumer of apps. But they should be able to explore their own data. Data literacy will therefore benefit from analytics meeting people where they are, with applications developed to support them in their own context and situation.

Start-ups can use these trends as the foundation of their move towards embracing information activism and becoming data literate.

Business models are changing because of the demands driven by data, and new platforms and technologies are constantly being introduced to help with the change – by catching “the other half” (i.e., less skilled information workers and operational workers on the go).

This will help usher not just businesses, but a generation into an era where the right data becomes connected with people and their ideas — that’s going to close the chasm between the levels of data we have available and our ability to garner insights from it. Which, let’s face it, is what we need to put us on the path toward a more enlightened, information-driven, and fact-based era.

Dan Sommer is a senior director at Qlik

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