How To Avoid Big Data Analytics Failures

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Initiatives for big data analytics can be a game-changer in many ways, since they can give you an insight on how to serve your customers better, blow past your competition and generate new revenue sources.  However, big data analytics projects can also have their share of failures which might result in a waste of time and money. Since managing blunders can be frustrating, such failures also result in the loss of talented professionals in the tech industry.

According to research carried out by Gartner in 2015, the big data project failure rate is 60%. Among the key contributors to this failure are people, rather than technology. The big question is, what you can do to avoid these project failures?

For you to succeed, you need to develop a workable strategy that will deliver business value from a big data initiative. You will also need to map out and acquire - or create - the missing and specialized skills required. Once you have addressed all the skill priorities and strategies, you will be able to move on to big data analytics.

From a basic business management perspective, some of the best practices in avoiding failures are obvious ones. For instance, make sure you hire the needed expertise or have good training in place. Ensure you have executive buy-in from the most senior levels of the company, and fully invest in all the technology needed. Failure to address such basic requirements will set you up for failure.

Now that you have catered to the basics, the only thing that stands in your way is how you will deal with the challenges and technical issues that come with projects. Below is what you can do to avoid failure in their execution.

Choose your tools for big data analytics carefully

Most companies experience technology failure, because most tech products bought and implemented prove to not be an acceptable fit for their aims. Any vendor can put the words ‘advanced analytics’ or ‘big data’ on their product descriptions just to make it more marketable.

It is essential to understand that products differ significantly, not only in effectiveness and quality but also in focus. You might choose a product that is technically strong, and it still ends up not being a good fit for what you need. While there might be some basic products for almost all big data analytics, such as storage architecture and data transformation, this sector also has multiple niches. This means you have to get products for the niches involved in your strategy for managing big data.  Some of the niches include business intelligence dashboard, real-time solutions, process mining, artificial intelligence and predictive analytics.

Before you decide to buy any big data analytics storage platform or products, you ought to first consider what the real business problems and needs are. Afterwards, you can select the products that are designed to address those specific problems successfully.

For instance, you might opt for cognitive big data products such as analytics using artificial intelligence for analyzing unstructured data, because of the complexity that comes with compiling huge data sets. However, you would not use cognitive tools for either standardized or structured data, because you can deploy one of the many products with the capability to generate quality insights in real time, and at a reasonable cost.  

Therefore, it is wise to run proof of concepts with two or more products before settling for the one you will use.

Ensure the tools you choose are easy to use

Big data analytics are complex, but the products relied upon by business users to make sense and access the data should not be complicated. You should provide simple and effective tools for your business analytics team to use for analytics, visualizations and data discovery. Additionally, do not provide programmer-level tools to the business users who are nontechnical. They might become frustrated and resort to the tools they had been using previously, which might not give the results you want.

Ensure your business needs are aligned with the project and the data

Big data analytics efforts fail because they might be a solution searching for a non-existent problem. Therefore, you must frame your business structure according to the needs and challenges of the specific analytical problem you want to address.

You can do this by involving subject matter experts that have strong analytical backgrounds, along with data scientists to define the problem early on in the project.

Create a data lake and do not be frugal on bandwidth

Big data analytics feature massive volumes of data. It used to be that only a few organizations had the ability to store huge amounts of data, much less analyze and organize it. These days  there are large-scale parallel processing and high-performance storage technologies available, both on-premises and in cloud systems.

Storage on its own is not enough, since you need a way to deal with distinct types of data feeding into your big data analytics. These repositories are commonly known as data lakes. Just as a real lake would be fed by several streams and contain numerous species of fish, plants and other species, a data lake is also fed by several sources of data and contains numerous data types.

However, a data lake should not be used as a dumping site. Make sure you are careful on how you amalgamate data to increase attributes in a more meaningful manner. You should develop a data lake where the ingestion, indexing, and normalization of data feature well-planned components of big data strategy. If your blueprint is not well articulated or understood, then your data-intensive initiatives are destined to fail.

It is vital you have sufficient bandwidth. If not, your data won't move from the different sources into the data lake fast enough to be of use. If you want to deliver on the promise of offering massive resources for the data, you not only need processing engines and interconnected nodes that can readily have access to the data generated, but also fast disks capable of millions of IOPS.

Real-time analytics require speed, whether in traffic routing or social media trends. Therefore, use the fastest connection when creating your data lake.

Ensure that security is designed into every facet of big data analytics

Most of the data being gathered, stored, analysed and shared is client information, some of it identifiable and personal. In the event that such information lands in the wrong hands, the result might be unhappy clients, a damaged reputation and brand, and most importantly the loss of money stemming from regulatory fines and lawsuits.

The security measures you put in place should include establishing basic enterprise tools, using data encryption when practical, network security and access management. Training on the use and access of data, as well as policy enforcement, should be a part of your security measures.

Prioritize on quality and data management

If you want your big data analytics project to succeed, you need to ensure you have good quality and data management. Failure to do this means the chances of your project flopping will be higher. Put controls in place that will promote accuracy, and ensure the data will be up-to-date and delivered in a timely fashion.


You need to demonstrate immediate value to your business from a project otherwise you, your managers and end users will be left wondering why you are not getting the results you anticipated. If you are looking for the best big data analytics solutions, contact our IT experts and data scientists at

If you have any comment or questions, remember to leave a post in our comment section below.


Posted 18 November, 2017


Software Developer

Lucy is the Development & Programming Correspondent for She is currently based in Sydney.

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