Big data is a loaded phrase. In the world of modern business, it seems that everyone knows they need to work with and take advantage of Big Data, but few know what that truly means or entails. At what point does data become ‘Big’? What are the implications for businesses that have to work with Big Data? How can companies realise its potential?

All of these questions seem to ignore a crucial point: Not all organisations are working well with data in general, let alone Big Data. ‘Small data’ is still an issue for many. In the broadcast sector, for example, innovation and development around data has been cautious and slower than most industries. Remember that it was only in October 2012 that terrestrial UK television finished switching over from analogue to digital.

>See also: Big data predictions

Now, all content and broadcast companies are furiously developing and diversifying their business models, from working out the balance between broadcast and online content to catching up with and integrating the opportunities of social media. All of this requires fluency and expertise with data.

Against such a complex backdrop, it’s important to step back and assess priorities. All businesses must understand why data is valuable, and the main considerations to bear in mind when tackling a data approach.

Extracting insights

Any business’s interest in data stems from the fact that it can be a powerful catalyst for growth. But in its raw state, it’s technically not worth anything – data only acquires its value through use. It must be analysed and interpreted, with the aim of extracting useful, actionable insights. These insights are where the business advantage lies, as accurate insights can then be used to inform and intelligently steer business decision-making.

Gone are the days of assumption and ‘following your gut’. Modern business decision makers need to have solid facts at their fingertips in order to make the critical decisions that keep today’s businesses ahead of the competition.

The value lying dormant in unused data is the reason why data scientists have become so highly prized and eagerly sought. Experts in the collection, storage, management, and analysis of data, they hold the keys to unlocking the insights that businesses crave. However, they’re also in short supply.

In 2015, a European Commission report predicted that Europe would need 346,000 additional data scientists between 2013 and 2020, in order to keep up with demand. While some universities now offer data science postgraduate degrees, this is just one step towards plugging the data science skills gap.

Some organisations have managed to elbow their way ahead of the pack, particularly those that lead in the spheres of technology and digital entertainment. These leading lights have generated vast quantities of data for years and already use this to guide their strategy on an ongoing basis.

>See also: Big data vs. privacy: the big balancing act

Netflix, for example, spends tens of millions of dollars per year on its data science division, as their business model relies on knowing their audiences and providing viewing recommendations. But Netflix isn’t unique in its need for insight, and while much of the data it analyses comes from consumers, it will undoubtedly be harnessing other data too.

The insights extracted from data can apply to every area of a business, from tracking supplier fulfilment times to examining trends in customer complaints on Twitter – or picking out online viewing habits. With such a broad range of applications, it’s useful for businesses to think of data as fitting into one of two categories: operational or consumer.

Internal, operational data

Operational data is information generated by the workings of the business itself. This could also be thought of as ‘internal data’, and includes payroll information, warehouse storage details, and experimental R&D data. Any data that is generated by the day-to-day operations of the company itself.

This data can often be overlooked, as organisations rush to understand the consumer. But this is where business efficiency lies. Analysing and understanding the processes within an organisation can show where new ideas and streamlining could have the biggest effect. Is video content being stored on the same, cutting edge system or in a variety of places, on old, unreliable equipment? How do sales numbers differ between regions, and if there are differences in sales, where else are there differences that could explain these discrepancies?

Using operational data to answer questions like these can improve – and even automate – time-consuming, inefficient processes. It can address unbalanced workloads and alleviate pinch points, helping a company to run more smoothly.

It can also pre-empt problems. Risk mitigation is a major factor for all businesses, especially those with an online presence, as there is zero tolerance for service outages nowadays. Analysing systems data when errors occur can reveal how issues occur in the first place, and by combining this data with machine learning and AI, business systems can learn to anticipate and address any hiccups before they become major issues.

External, consumer data

Consumer data is so highly valued because it provides insight into the people to whom a business is selling, or attempting to sell. It’s largely external to the company, and could include age information, addresses, and spending habits.

It’s this data that many businesses, particularly those in the broadcast and entertainment sector, focus on with intent. This laser focus comes from the need to understand who their customers are, where they are, and how they’re engaging with the company, and its products and services.

>See also: How big data and analytics are fuelling the IoT revolution

However, collecting this data can be problematic. General trends and anonymised data can be gathered from various sources, such as social media platforms and analysis tools, but most businesses want specific data – down to the individual level – and this means gathering it from consumers themselves. The key problem here is that many consumers haven’t wanted to share their information.

Thankfully, customer data is being provided less grudgingly nowadays, but companies still have to actively pursue it. Channel 4 provides a good ‘best practice’ example for how to gather data from customers without upsetting them, based around two ideas: incentivisation and reassurance.

The company needed to track its customers’ viewing habits, but with people watching anonymously online, the data and insights were limited. Yes, it was possible to see the most and least popular programmes, but getting that deeper insight into viewer demographics wasn’t possible.

Channel 4, therefore, introduced a registration process with a clear benefit – setting up an account and signing in would provide you with access to content you wouldn’t otherwise we able to see. There’s the incentive.

At the same time, the company promised that customer data would be used in very specific ways and wouldn’t be shared with anyone else. This reassurance that data is being treated respectfully, and not flung about a wider ecosystem, helped to encourage more hesitant viewers to set up an account, and unlocked the deeper data and insights Channel 4 was hoping for.

>See also: How can a business extract value from big data?

With these insights, Channel 4 is then able to better sell ad opportunities to brands, as deeper, more detailed information allows marketers to more precisely see the audience demographics they’re reaching, and avoid wasted spend.

The elephant currently sat in the corner of the server room is the General Data Protection Regulation (GDPR). The EU’s new regulation aims to strengthen data protection for all consumers within Europe, which is causing problems for those organisations that need to completely overhaul their practices.

The requirements set out in the GDPR must be met by May 2018, in order to avoid a hefty fine, but the guiding principles at the heart of the legislation are inarguable. Organisations must only hold consumer data they’re going to use, they must know where it is, and they must make sure it’s secure. At the end of the day, meeting these requirements comes down to having the right technology solutions in place, and the right people managing them.

Tailored tools and specific solutions

The tech vendor marketplace is in a constant state of flux, with funding rounds, acquisition, and IPOs happening on a regular basis. This continual change is a symptom of the levels of innovation and expertise that are being funnelled towards the challenges of working with and taking advantage of data. Now, rather than just a small handful of colossal companies controlling the entire ‘data marketplace’, there are also hundreds of specialist players, each offering different solutions –
many focused on particular industries or markets.

>See also: How big data is transforming the property market

For those businesses looking to make a change in how they approach their data, the possibilities have never been so wide open and exciting. But there are two key points to bear in mind when choosing which direction to go: Make sure your vendor understands your particular business needs, and ensure that the tool they’re providing is right for your organisation now and for any future plans you may have.

To answer one of our original questions, data arguably becomes ‘big data’ at the point where there is too much data for an organisation to handle. And that happens when the organisation is gathering more than it needs or can keep track of. With the right tools, and the right people, many organisations can avoid this problem entirely.

Sourced by Stuart Almond, head of marketing and communications for Media Solutions, Sony Professional Europe