Data science projects
Home technology Top Five Reasons for the Failure of Big Data Projects

Top Five Reasons for the Failure of Big Data Projects

7 min read
0
169

More than 87% of the data science projects taken up by businesses fail to move past the preliminary stage, reveals the latest infographic released by the Data Science Council of America (DASCA).

Why might that be?

Technology? Maybe. The field of data science is new, and the arrival of fresh tools and technologies is common. Technology is the first step in the implementation of big data projects. Opting for adequate tools, getting hands on the right data types, storing it, structuring the unstructured data, and processing it, can be challenging.

However, such a high failure rate cannot be all impinged upon technological shortfalls alone. There is also an element of inadequate management.

For data science projects to be successful and scalable, planning, headship, and the team have to blend in with the right tools.

Investigating the ground reality, DASCA catalogs unique pitfalls and risks linked to big data project failures and giveaway the corporate strategies for successful big data project implementation. From its infographic – ‘Tips to turn big data into success,’ we look at five of the many reasons cited for big data project failures.

Fueling growth with Big Data projects

Big Data is at the foundation of megatrends – from the speedy rise of startups in less than a decade, and emerging technologies – AI, ML, and more – data is the anchor. Business (of all sizes and types) are looking to tap into data goldmine to improve operational performance and manage volatility: Manufacturers for improving supply and logistics, healthcare for drug testing, finance for predicting valuable assets, and so on.

According to Gartner, by 2022, 90% of the corporate strategies will mention explicitly that information is a critical enterprise asset and analytics is an essential competency for their business. In these times of infinite intelligence, transforming data into insights with success require watertight Big Data project management strategy.

What derails Data Science projects?

Data is a rich reserve organization always had. Success with exploiting this high-volume data for business optimization and business development requires data science professionals to overcome these challenges.

Challenge #1: Not addressing the root cause

Asking the wrong questions can be highly detrimental to an organization’s successful entry into data science industry. This is where certified data science professionals who possess the domain knowledge of data science, as well as a deep understanding of the industry, can be leveraged.

Challenge #2: Using technologies that are cool, but not useful.

There are numerous tools and technologies available for big data processing and analytics. Can the tools and technology chosen by you deliver the desired solution? It is important to check that. Furthermore, even if sometimes the technology used can deliver a prototype, it might fail to give real value when it comes to scaling the solution.

Challenge #3: Poorly designed models that are not robust

There is a swarm of results that might be good, but cannot be reproduced. The reason could be many – complexity of the experiment or use of not reusable techniques. The key to a successful implementation of big data analytics is to keep in mind that it is not a one-time exercise. Analytics that is not robust is of no good use. Sustaining a mixed team of professionals with not only IT and data, but industry and consumer behavior understanding can take a data science project off the ground.

Challenge #4: Lack of a standardized data science process

While a project stretched for too long can eat time and money, allowing little time for data analytics implementation can also be counterproductive. Establishing a standardized process is a must for a successful data implementation strategy.

Challenge #5: The solutions are too complex

Presenting a complex solution in the simplest of language is the real success. Data scientists sometimes devise complex models where a simple model could work just as good or at times better. When a team goes out with the focus of not digressing from the right path, simple and effective solutions can be reached. It requires not complicating the problem and staying focused on the big picture.

What solutions can you adopt to beat these challenges? Recognize the right metrics and KPIs, establish proper data processing procedures, use the right tools and techniques, and other solutions like these can be adopted.

Check more on the tips for successful data science projects implementation in the infographic, here.

Data Science projects

 

Leave a Reply

Your email address will not be published. Required fields are marked *

Check Also

Welding Helmet For Beginners

Introduction: A welding helmet, a covering hat or headgear, which is worn on the head in o…