FOLLOW

FOLLOW

SHARE

5 Reasons Your Big Data Strategy Is Failing

Why aren't you seeing returns from your data project?

9Sep

Big data is nothing new. The majority of companies now collect a wealth of information about their customers and processes, leveraging the insights to streamline operations, drive marketing, and ultimately increase profits.

The opportunities made available by Big Data are truly astounding. However, many companies are still not seeing a return on their investment in data. In a survey of senior business leaders at 1800 large companies in North America and Europe, only 4% were classified as being ‘data elites’ and successfully using data to improve business performance. More than a quarter reported seeing ‘no or little benefit’ from their data initiatives. Similarly, in a survey by business transformation consultancy Moorhouse, of the FTSE250, just 11% of such organizations said they believe they are effectively leveraging big data to inform their strategic decisions. There are a number of reasons for this, all of them easily fixable.

1. Stuck in ‘gut instinct’ mode

It is natural that an executive with more than 20 years experience will believe they know best, in spite of all evidence to the contrary. Consequently, they will ignore the wealth of data they have at their disposal. Fortune Knowledge Group found that 62% of business leaders still say they tend to trust their gut, while 61% believe that real-world experience is more important than analytics for decision making. While understandable, from a business sense it is exceptionally reckless. All decisions should be supported by strong evidence, not by opinions and intuition. The first question should always be ‘where is the evidence?’, if not, you are writing cheques your business can’t cash.

2. Lack of C-suite buy-in

In a similar vein to problem one, if the C-suite isn’t truly on board with a data project, it will go nowhere. Obviously, it will likely result in a lack of investment, but the real problem is that there will not be the kind of data-driven culture instilled throughout the organization that enables data initiatives to be successful. A data-driven culture will ensure that everyone, from all backgrounds, is drawing insights from the data, not just analysts. Employees must put data at the heart of their thinking and be willing to share data sets with other departments. This attitude starts from the top down, and the C-suite must ensure that they have organized their operations and strategies in place to create a culture of evidence-based decision making.

3. Big data silos

Big data silos are a particular problem for incumbents, transferring data from legacy systems and disparate sources. When data is organized into silos, it ends up being separated from other data, isolated from broader processes, staff, and - most importantly - from broader decision-making. When an organization unifies its data and it all flows together, information becomes a strategic asset, giving a clear advantage over the competition.

4. Focusing on the wrong data

In their rush to collect all the data they possibly can, organizations often actually end up with too much data, making it difficult to use efficiently and discover anything. The key is to collect only data that’s really necessary and will actually aid the business. It’s also important to understand that individual data is not the most valuable resource, rather it’s the data that shows what an entire demographic is doing.

5. Not taking action

Data is nothing without action. It sounds like a cliche, but it’s very much true. Many companies are successfully collecting data and discovering insights, but they are not translating this into action and are failing to learn from it, essentially rendering the whole ordeal redundant.

Comments

comments powered byDisqus
Turkeyss

Read next:

What’s On Data Analysts’ Plates This Thanksgiving

i