Organisations that utilise data can grow 50% faster than their peers, according to a study by Dell. By optimising big data in marketing efforts, decision making and process optimisation, companies can make more intelligent decisions faster – as well as leverage their resources more effectively.

Big data is an easy way for organisations to identify purchasing and engagement patterns in their customers. Through big data, companies can score better leads and identify customers that are most valuable.

In relation to marketing efforts, 41% of organisations are using big data to improve their marketing exercises while 37% of companies are optimising their marketing strategies in general with other companies focusing on social media campaigns.

But there is another statistic that is important from this Dell study – 39% of organisations that established a big data plan were not certain what the benefits of their big data strategy actually were. And this is why many companies are not using big data.

Failure to Turn to Big Data

Despite the tremendous advantages of big data, only 49% of large businesses are currently looking towards big data implementation. This is compared to 21% of small businesses and 19% to 26% of mid-sized businesses.

Interestingly, businesses with 50 to 249 employees are far more likely to implement a big data plan than businesses with 250 to 999 employees.

Data fuels AI

Meaningful artificial intelligence (AI) deployments are just beginning to take place, according to Gartner Inc. The Gartner’s 2018 Chief Information Officer (CIO) Agenda Survey shows that 46% of CIOs have developed plans to deploy AI but only 4% have successfully done so. 

Google and Amazon didn’t make the cut. Microsoft didn’t make the leaders quadrant. This seems strange to many industry observers. Google was the only leader in The Forrester Wave™: Insight Platforms-as-a-Service as of end-2017.

Companies have long been aware of the urgency to properly harvest the full value of data. Investments are pouring into big data, analytics and AI, driven by the fear of disruption from data driven digital competitors in an increasingly competitive global landscape.

Becoming a Data-Driven Organisation

One might consider the use of technology or having a solid strategy for data quality, governance, and access, but perhaps the most important factor in becoming data-driven is having the right leadership to create a culture that places data at the heart of the organisation.

The management of the organisation will have to clearly define a strategy which will lead to meaningful success measurements and key performance indicators. The data literacy gap should be addressed as talent who will not only use but adopt data science.

Next would be the executive buy-in and support given an organisation needs a committed and involved leadership team to ensure tangible progress toward fostering a data-driven environment.

Finally, the most important part is to ensure data collected and analysed is being used and applied to benefit the organisation. There must be a belief the data will assist in making business decisions, otherwise all the effort will be for naught.

That’s why getting the people aspects right is so important. A few organisational best practices that will facilitate individual change towards becoming data-driven include incentives, empowering the staff to use data to solve business problems and developing analytics literacy.

Unlocking the Value of Data

Unlike traditional enterprises, data-driven organisations don’t grow linearly, but exponentially. Just look at the spectacular growth of companies like Amazon and Google who have built their entire business models around the exploration and exploitation of information.

What these companies have in common is a data-centric approach that goes beyond operational excellence. This requires them to put data and analysis front and centre in everyday business processes, and to think beyond silos and even the company’s own walls (literally and metaphorically) to build meaningful collaborations.

Therefore, even though 85% of global companies are trying to be data-driven, only 37% of that number say they have been successful. Thus, it is imperative that in this information generation, there should be laws on how to organise, develop, manage and engage businesses in the 21st century.

On account of how precious data will be as compared to oil or gold, there must be paradigm shift towards big data in order to gain competitive edge and keeping extinction at bay.

The Challenges of Becoming Data-Driven

Privacy regulations: Data-driven organisations need to have a profound knowledge of rules and regulations surrounding data, i.e. the Personal Data Protection Act 2010 (PDPA) so as to avoid encroaching on privacy or stepping over boundaries when collecting and analysing data.

Persistent silo thinking: While there is a growing consensus that data should be at the heart of everything businesses do, the majority of organisations don’t have a solid, company-wide data strategy in place, thus continue to keep information siloed from departments and its people.

Lack of data integrity: Ample data can be found in most if not all companies for business leaders to make insightful decisions, but most of the information is poorly managed and improperly exploited.

Having the right skills: According to a study by Gartner, companies struggle to meet the data skills requirements they need. There is a fine art to data science that requires more than number-crunching. Finding data-savvy people with the right business acumen to understand the company’s objectives and have profound technological knowledge is not an easy feat.

Identifying the right technology: An over-abundance of data solutions is leading to many companies in bewilderment while looking for a good fit for their specific business case. Identifying the best-fitting technologies, investing and implementing them successfully remains one of the top challenges on the road to data-driven greatness.

About the Author: Sharala Axryd is the founder and CEO of The Center of Applied Data Science (CADS)

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