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Hands down, data is one of the biggest assets for businesses today. There is one caveat, though: if handled and analyzed properly. If treated in the right way, big data emerges as a source of meaningful insights that help companies drive strategic decisions at the speed of business and hence fast-track business growth. That being said, a good number of companies have a hard time implementing analytics to translate information into actionable insights and then value. So, what are those most common roadblocks that prevent businesses from getting the most out of their analytics initiatives and massive amounts of data accumulated within their organizations? Let’s dive right in.

Data Silos

While quite a big number of businesses have already adopted and are using analytics in some way, the overwhelming majority of organizations are still struggling to reap enormous benefits big data analytics can bring. The reason: data silos. Huge volumes of raw fragmented data, disparate systems and isolated data islands create a roadblock to implementing big data analytics in the first place.
Hidden across multiple disconnected systems and applications throughout the organization, petabytes of isolated data become unavailable or incomplete for analytics, insights and hence strategic decision-making at the moment of need
On top of that, volumes of structured and unstructured data growing at a rapid pace every day exacerbate the problem making the gap between data and business analysts even wider and more unbridgeable.

Legacy data pipelines

A data pipeline serves as a processing engine that transports raw data from database sources to data warehouses for further analytics. Highly efficient and modern data pipelines are just as critical for the success of big data analytics since they enable to swiftly extract information from its source, convert it into a usable format, and load it into destination systems where data is analyzed for insightful decisions in real-time. This drastically cuts down the time from data capture to analysis to insight. Rigid and slow, legacy data pipelines, in their turn, feed information in batches. This results in high data latency, forcing data scientists to simplify data or use a limited data set for analysis, and hence allows gaining only superficial insights providing little value. Rigid and slow, legacy data pipelines, in their turn, feed information in batches. This results in high data latency, forcing data scientists to simplify data or use a limited data set for analysis, and hence allows gaining only superficial insights providing little value.

Vendor lock-in

Closely related to the legacy infrastructure challenge, vendor lock-in is another barrier standing in the way of big data analytics success. As your big data maturity grows, the resources of your BI platform might become insufficient to meet your new big data requirements. The inability to update the platform or expand its analytics capabilities only adds to this problem. And here is the worst part: once you decide to migrate to an alternative solution, you will be unhappily surprised to discover that your current vendor is not willing to share your data with it. That is what vendor lock-in is all about. The point is that for fear of losing business to competitors, some big data vendors tend to keep data to themselves, thereby locking clients into their ecosystems and impeding their business growth.

Leadership denial

Let’s face it: big data analytics implementation is not purely a technical challenge. Deep down, this path is about securing leadership buy-in from the top down in the first instance. No doubt technology matters. But if senior leaders don’t see a rationale for or are not ready to weave big data analytics into the fabric of the operational processes, any analytics initiative undertaken by big data evangelists is destined to fall flat. While big data has been around for quite a long time, a whopping number of organizations have not yet achieved a transformational level of maturity in terms of data and analytics. The fear of change or big investments, a lack of resources or inability to grasp the business value of big data analytics – for whatever reason, a significant number of business decision-makers are still sitting on the fence about embedding Big Data solutions into their operations.

Lack of data literacy culture

Alongside leadership buy-in, the success of big data analytics initiatives rests largely on establishing an enterprise-wide culture of data literacy. The job of cleaning up and preparing big data for analysis, sifting through this data and finally analyzing often seems like an impossible feat for human minds to accomplish. At other times, business users trust intuition rather than data when it comes to strategic decision-making. Whatever the case, the reluctance to embrace big data and data analytics can be largely attributed to a lacking data literacy culture. While the C-suite can be fully on board, without culture change and a high level of organizational maturity all analytics efforts will come to nothing.

Bottom line

This is by no means an exhaustive list of all impediments businesses might encounter on the path to big data analytics success. In each specific case analytics initiatives are being held back by a different set of challenges. What barriers exactly might prevent businesses from becoming data-driven depends, by and large, on the current level of organizational maturity.
Anyways, it all boils down to one of the two aspects – people or technology. Or both.
And one more thing to be aware of: removing these obstacles is not a matter of choice – businesses have to get all barriers out of the way to set the stage for a successful big data analytics project. Embarking on a big data analytics journey on your own is a pretty big undertaking. Having a big data vendor orchestrating the whole thing will make a big difference. With in-depth expertise in big data implementations, ITRex can help you clearly define where you are standing in terms of both culture and technology. Our seasoned big data experts will help you outline a holistic big data analytics strategy, opt for the right big data provider and implement the right technological solutions for quick wins in your big data analytics project.
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