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Data Analytics Top Trends In 2018

What is 2018 going to bring for analytics?

27Nov

The last year has seen the world of big data consumed by intrigue, as accusations abound that dark forces are using it to subvert democracy and tear apart the very fabric of society. The collection and use of our private data by the likes of Facebook has come under increasing scrutiny and its public perception is generally negative, with stringent regulations such as GDPR set to be implemented in 2018 that may go some way to correcting this, but will greatly complicate how organizations approach data.

It is not all bad news, though. In a NewVantage survey earlier this year, 48.4% reported that their firms are achieving measurable results from their big data investments - the first time the survey has found a near majority since it began in 2012. Furthermore, a massive 80.7% of executives characterized their big data investments as 'successful,' compared to just 1.6% who said they believed they had failed.

In 2018, more and more organizations will reach data maturity. We will see analytics practitioners face many of the same challenges they've been dealing with in recent years, as well as a few new ones to boot. Disruptive forces will continue to conspire to produce fresh problems on a daily basis that will make the use of data analytics both more difficult and more vital. Companies will collect more data than ever, and there will be new technologies and strategies to help exploit it to its full potential.

We’ve outlined our predictions for what 2018 will bring analytics.

Analytics Adoption Rises Among Small Companies

Large companies long ago realized the importance of data and analytics. In an IDG study last year, 78% of larger employers agreed data collection and analysis have the potential to completely change the way they do business. However, small companies are still behind in their efforts. According to an SAP-sponsored global survey of small businesses, many are still in the early stages of digital transformation. Further research from One Poll found that 56% of SMEs rarely or infrequently check their business’s data, while 3% have never looked at it at all. 

This should change in 2018. In the SAP survey, 73% and 87% of small and midsize businesses surveyed indicated that their expectations regarding technology investments were met or exceeded. With the cost of data analysis and visualization technologies falling and investments bearing fruit, this year should see the adoption of data analytics extend to even the smallest of companies.

Outsourcing Of Analytics Increases

One option for SMEs unable to fund full-scale programs is to outsource them to an outside agency that specializes in data analytics. 

Outsourcing analytics is an excellent way of enhancing your data capabilities when you lack the necessary funds, making it ideal for small companies. In 2018, though, the ever-growing problem of a lack of qualified data practitioners will also likely see many larger organizations look to the insights-as-a-service market. According to a recent PwC study, 69% of employers by the year 2021 will demand data science and analytics skills from job candidates, yet just 23% of college graduates will have the necessary skills to compete at the level employers demand. In a recent report sponsored by Dun & Bradstreet, 27% cited skills gaps as a major obstacle to their current data and analytics efforts. Of these, 60% said they are already using third parties to support organizational bandwidth and 55% are outsourcing some or all of their analytics needs. Forrester also predicts that as many as 80% of firms will rely on insights service providers for at least some of their insights capabilities in 2018.

Tony Fross, VP and North American practice leader for digital advisory services at Capgemini Consulting, notes that, 'The decision to outsource is always about what the core competency of your business is, and where you need the speed. If you don't have the resources or the ability to focus on it, sometimes outsourcing is a faster way to stand up a capability. So how do companies today make the leap to light speed and become big data analyzers? Do they go outside and hire data analysis consultants or try to develop the capability in-house? The fundamental question must be "how business-critical is the data?"'

Qualitative Data Is On The Up

Organizations will realize in 2018 that quantitative data alone cannot help you truly understand customer behavior and market trends as it does not allow you to properly understand human emotion. It does not account for the ebbs and flows of people's motivations and feelings and its insights can quickly become invalid as a result. Qualitative data bridges these knowledge gaps. It is the information found in the unstructured data of online reviews, social media, and so forth, that provides the context to help understand why something is the way it is and if it is changing.

When we rely solely on big data, we end up with a warped sense of the world in which human beings are simply numbers to be fed into an algorithm. This is not to say it is useless, nor that in many cases it can be used alone. It is still a powerful and helpful tool that companies should invest in. However, companies should and will also invest in gathering and analyzing qualitative data to uncover the deeper, more human meaning behind big data. Data scientists and ethnographers need to collaborate to ensure that big data has its groundings in actual human behavior, not just what a machine thinks it should be in perpetuity.

Transforming this qualitative data into quantitive form is also becoming increasingly easy to do, with machine learning technologies such as Brainspace's now able to mine and visualize the unstructured data. It is not easy and there are still many challenges. As Dr. Kirk Borne, principal data scientist at Booz Allen Hamilton, wrote in a blog post for MapR Technologies Inc, 'there are far more subtleties and intricacies in language that we can use to extract deeper understanding and finer shades of meaning from our qualitative data sources about our customers, employees, and partners.'  However, it is vital, and this year should see it become a much bigger deal.

GDPR Leads To New Opportunities

On the 25th May 2018, the EU General Data Protection Regulation (GDPR) comes into full effect after years in the making. The GDPR is the EU's latest rewrite of its data privacy laws. Its impact will be felt by organizations across the globe, applying to every piece of data that touches the countries that signed - regardless of where in the world the data has been captured and analyzed. This has had tech giants up in arms at what they perceive to be a war on their power, and they will have their work cut out for them continuing to do so with the new rules.

This has many ramifications for data teams in terms of the data they can collect and how they use it, but it also presents an opportunity to companies that prepare fully and embrace GDPR, not just in terms of compliance but in rebuilding some of the trust with consumers that has been lost in recent years. 

It will also help companies become better at managing their data. As CFO of SAP, Emmanuelle Brun Neckebrock recently wrote on this site, 'Data is one of your company’s most valued resources, yet one of the most poorly managed. It’s the golden thread that runs through the entire organization, and in most instances, it’s managed casually and inconsistently, depending on individual employees and departments. You wouldn’t let your revenue, products, or equipment assets be handled that way, so data (given its inherent value) shouldn’t be any different. It warrants the same due care and attention. GDPR legislation is unique in that it allows you - OK, forces you - to transform the way you handle data across the whole organization, managing associated risks and compliance. In doing so, it’s actually strengthening your ability to compete on the digital playing field, making you more agile for long-term success.'

Companies Look To External Data

External data is any data generated from outside an organization. It can come from a variety of sources. For example, the US government alone makes available more than 131,000 datasets on the federally-run website data.gov. Another example is weather, which is particularly useful in supply chain management. UK based supermarket chain Tesco, for example, is renowned for their use of weather data to drive richer insights that help them to predict sales and stock requirements. They reported in 2013 that they had managed to save £6m ($7.5m) per year and reduced out-of-stock by 30% on special offers. In fact, in a recent survey of supply chain professionals by the UK Met Office, 47% cited weather as one of the top three factors external to their business that drives consumer demand. Of these, 57% said they had better sales forecast accuracy, 51% that they had better on-shelf availability, and 43% that they had reduced waste.

In 2012, Forbes writer Dan Woods argued that companies were suffering from what he called ‘data not invented here syndrome’, and were failing in their data initiatives because they focused solely on using data created inside the four walls of their business. This is a situation that has largely not improved. This is, to some extent, understandable. There are real risks around the use of external data and it is not right for every industry, but it can be a real driver of growth when applied correctly. In 2018, greater accessibility to external information and greater maturity in data programs should see those organizations for whom it makes sense far better positioned to use it.

Mario Trescone, Senior Director of Business Intelligence and Data Analytics at YMCA of the USA, notes that, 'Today, I believe most companies are aware of the importance of external data to stay informed and competitive, especially the large to mid-size organizations, however, they may not be structured or disciplined enough to know how to capture or apply external data within their Business Intelligence function. It really depends on how the organization is structured, its data culture, and how they ultimately are defining the Business Intelligence role. Throughout my career, the positions I have held in the realm of data analytics and research, connecting external data with operational data was/is at the heart of almost everything I do. How else do you put context around the trends you are seeing related to the current and forecasted demand of your products and services. Having a thorough understanding of your market, using both primary and secondary data, allows you to maintain a pulse on the market, ensuring relevancy of your organization today and into the future. So for anyone in the Business Intelligence role, if you are not connecting your operational information to external data sources you need to start.'

NLG Helps Business Users Better Understand Visualizations

Natural Language Generation (NLG) and Natural Language Processing (NLP) are often wrongly used interchangeably and misunderstood by those in business. While NLP focuses on understanding what ideas are being communicated by analyzing textual data for patterns, NLG is a branch of AI that communicates the findings and insights discovered by NLP by translating them into natural language. This is now being integrated into analytics tools to work alongside data visualizations in order to provide mainstream users with a clearer narrative, demystifying data and communicating insights for you in real time so action can be taken. And BI and Analytics vendors are investing significant sums on Advanced NLG technology to complement their data storytelling. Indeed, by 2019, Gartner predicts that NLG will be a standard feature of 90% of modern business intelligence and analytics platforms.

Michael White, an associate professor at Ohio State University, believes that this means NLG is finally on the precipice of entering the mainstream, arguing that, ‘There’s growing awareness that masses of data and visualizations are not really helpful if they can’t be explained and made relevant. I’d say the time has finally become ripe for natural language generation to have commercial success.’

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