2016 Data Trends and 5 Reasons Companies Fail to Drive Value with Data

In this special guest feature, Anil Kaul, CEO of Absolutdata, discusses where he thinks companies are going wrong and why they are unable to drive value from big data. He also discusses strategies companies should use in 2016 to implement big data. Dr. Anil Kaul is the CEO and co-founder of Absolutdata. A prominent and well-known personality in the field of analytics and research, Anil has over twenty years of experience in marketing, strategic consulting and quantitative modeling. Before starting Absolutdata in 2001, Anil worked at Personify and McKinsey & Company. He has a PhD in quantitative marketing from Cornell University.

The most notable big data trend for 2016 and beyond is the continuing proliferation of data from a variety of sources — including the web, social media, video, transactions and much more — and the rapid rate of adoption of big data and analytics solutions. Experts predict 43-fold growth in data volume between 2009 and 2020, when total data is expected to reach 43 zettabytes.

Data velocity is also increasing for business analytics users. Last year, industry analyst Gartner predicted that about half of all analytics implementations will mine real-time data by 2017. But are companies deriving the value they need from their analytics implementations? The picture is mixed. A Teradata-McKinsey study found that 67% of senior data and IT decision-makers in large organizations surveyed reported that analytics have a significant and positive impact on their revenues. But challenges remain for companies that are trying to drive data value:

1. Speedbumps at the start of the big data journey: It’s important to get data strategy right at the outset. There are two typical approaches, a traditional top-down strategy in which the company identifies a business problem and applies big data as a solution, and a bottom-up approach where the enterprise integrates a solution and then uses the data to identify patterns and issues. A hybrid approach where companies implement a bottom-up strategy and then apply data to resolve issues in a top-down manner is the optimal method.
2. Delays due to decision-making around technology tools: There are many decisions related to technology infrastructure that must be made prior to implementing analytics, and this can cause major delays for companies that are deploying a big data strategy. Choosing the right technology depends on the overall data strategy and analytics goals; it’s highly situational. Problems at this stage usually occur not because the company chooses the wrong technology but rather because making a decision takes too long and wastes valuable time.
3. Issues with configuring the data science team: Modern analytics is complex, and it’s rare to find a person with all the requisite expertise, which includes data, math, organizational and business skills. Instead, companies must put together a team with complementary skills, but this can be a significant challenge. It’s important to find the right balance of technical and business skills. It’s also critical to balance the need for centralization with the imperative to integrate within the larger organization. Finding the right skillsets and balance can delay a company’s ability to drive data value.
4. Problems with data quality and complexity: Occasionally big data implementations fail because of issues with the data itself, which is often raw and in multiple formats, including, for example, machine-generated data delivered in non-standard form. When confronted with this issue, companies often rely on manual, labor-intensive data clean-up techniques. They become frustrated with the time and resource commitment required and conclude that it’s not worth it. There’s no silver-bullet solution to this problem; specialized expertise is required to clean, load and integrate data properly.
5. Lack of focus on decision-making: Another roadblock companies run into after implementing a big data strategy is focusing too closely on insights and modeling and too little on actual decision-making. For decision-makers, too much information can be distracting, which is why modern map applications don’t present users with every piece of data that is used to create the optimal course but rather the suggested route only. Companies need a decision-engineering solution to keep key decision-makers focused on using data to drive value.

Topline data trends for 2016 are all moving in the same direction — an upward trajectory — as the volume of data grows and the speed of delivery accelerates. Companies that find the technology and expertise they need to efficiently implement and effectively use a big data and analytics solution are saving millions in operating expenses and driving millions in new revenue.

But the big data journey won’t be smooth for every company, and those that don’t negotiate the bumps effectively risk failing to maximize the value of their data. Company leaders who keep these five potential pitfalls in mind have a roadmap to big data and analytics success and will find themselves on the path to better decision-making and higher profits.

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