Why Data Analytics Projects Fail (Part 2)

This blog continues where we left of in a recent post on why data analytics projects often fail. Across virtually all industries, companies that are outshining their competitors are the ones who are on the bleeding edge of big data analytics. While many businesses are ramping up their efforts to instill more data-centric practices, an alarming number of these projects fail. Here we discuss a few more elements that contribute to big data projects failing within companies.


1.Lack of direction

Launching a big data project, or any for that matter, isn’t the be all and end all of your undertaking by any stretch of the imagination. If your business executives, and importantly, your IT complement don’t have a solid grasp of the pain points that your company (and industry, for that matter) are experiencing, then the technologies and solutions won’t be set up in a way that address your challenges.

A clear understanding of how big data will play a role in advancing your business objectives is a pre-requisite for any hope of success. Without this, projects often fail to gain traction and ROI is sadly scarce to non-existent.

2. Preparing for the data influx

Historic, present and future information all comprise the makings of a data analytics solution. In order to gain 360-degree insights into things such as customer behavior, operational intricacies, marketing and sales performance, businesses need wide spectrum views of their organizations.

This more than likely involves having massive quantities of data readily available (whether on-site or in the cloud, or a combination thereof) to understand how business units interplay, how they perform or where improvements can be made. The truth is that most companies still struggle with the high velocity of information that has been unleashed on them and suffer from what has becoming known as the “drowning in data” syndrome.



3. The “specialist” gap

When any big data project is launched, the polar opposites within an organization need to be aligned with the goal of achieving the same outcome. On one hand, you have corporate executives who want to see tangible, bottom line results, and on the other you have analytical data scientists. These two sets of people have very different goals, challenges and expectations related to data analytics.

When concepts, ideas and information get lost in the dialogue between these two groups, it results in big data project failures. Being able to communicate the right information to each other can go a long way in reducing communication gaps and break-downs that lead to disappointing results.

Having to rely on data scientists and train new staff to help your business understand your analytics platform is bad for business. If you need answers to questions that only technicians can answer, then decision making can often be delayed for months, which will hurt performance. This is perhaps one of the reasons why search-driven analytics solutions is gaining in popularity due to their ability to allow business users to query data through an intuitive search-type front end.


4. Too many different platforms to monitor and manage

Instead of having one integrated system that aggregates all their data, many IT departments still rely on a variety of tools to meet all of their needs. There is one tool that gets used for visualization purposes, another one for machine learning, and yet another tool for Enterprise Resource Management data, for example. The problem with this approach is that these IT departments are continually adding new tools and platforms that need to be maintained as well as monitored, and it makes getting answers to important questions a lengthy and cumbersome process.

Another common pitfall of this approach is that the software that has been proposed is inadequate. If your team isn’t familiar with the software, the learning curve involved in becoming proficient in it may be too long. Your team could also be resistant towards adopting software, or the software may be configured inadequately for your business needs. This results in delayed delivery and poor performance.


Fortunately, analytics platforms are evolving

In order for data analytics to be effective, the platform that you use needs to be intuitive and it needs to seamlessly integrate all of your data assets with each other. This makes the process of uncovering new opportunities easier as IT staff don’t have to learn new tools and applications and reinvent the wheel each time a query from a business client is lodged.

Tellius is bringing search-driven analytics to businesses across all industries. We’re introducing the power of search to the big data stage and giving business users the power to explore data in ways that are intuitive and effective – no science degrees required. To learn more on how search-driven analytics is reshaping the world of business intelligence, contact us.


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