Ways to Prevent Big Data Failure

Big data analysis initiatives can make a huge difference in many ways. They can provide insight into how to better serve customers, beat your competitors, and generate new revenue streams. Big data analytics projects are not without their faults. This can lead to a loss of time or money. Management blunders can be frustrating and can lead to the loss of skilled professionals in the tech sector.

According to Gartner's research, the failure rate for big data projects is 60%. This failure is more likely to be due to people than technology. What can you do to prevent these project failures from happening?

To succeed with big data initiatives, you must develop a strategy that delivers business value. Also, you will need to identify and acquire the specialized skills that are required. After you have dealt with all your skill priorities and strategies you can move on to large data analysis.

The best ways to avoid failures in business management are simple. For example, ensure that you have the right training and expertise. You must have the support of senior executives and investors in the latest technology. You will fail if you don't address these basic requirements.

After you've covered the basics, all that is left is the technical aspect of projects. Here are some tips to help you avoid failure when executing them.

Be careful when choosing your tools for big-data analytics

Technology failure is a common problem for companies. Most tech products purchased and implemented fail to meet their needs. To make their products more marketable, any vendor can add the words "advanced analysis" or "Big Data Analytics" to their product descriptions.

It is important to recognize that different products have different focuses and effectiveness. Even though a product might be technically sound, it may not be the right fit for your needs. There are basic products that can be used for big data analytics such as data transformation and storage architecture, but there are also many niches. You need to find products that fit your strategy for managing large data. These niches include real-time solutions, business intelligence dashboards, predictive analytics, process mining, and artificial intelligence.

Before you buy any big data storage products or platforms, it is important to identify your business' real problems and needs. Then, you can choose the products that address these specific issues.

You might choose cognitive big data services like analytics using artificial intelligence to analyze unstructured data. This is because it reduces the complexity of assembling large data sets. Cognitive tools are not suitable for structured or standardized data. Instead, one of the many products can be deployed to provide quality insights at a fair price and in real-time.

It is a good idea to test your concepts with at least two products before you settle on the one that you want.

Make sure the tools you select are easy to use

Although big data analytics can be complex, the products that business users rely on to access and make sense of the data should not be. Your business analytics team should have easy-to-use tools to help with data discovery, visualizations, and analytics. Non-technical business users should not be provided with programmers' tools. They may become frustrated and use the same tools that they used previously. This might not produce the results you desire.

Make sure your business requirements are aligned to the project and data

Because they may be looking for a solution to a problem that is not there, big data analytics fails. You must structure your business according to the requirements and challenges of the analytical problem you are trying to solve.

This can be done by including subject matter experts with strong analytical backgrounds and data scientists to help you define the problem early in the project.

Make a data lake, and don't be stingy with bandwidth

Analytics using big data implementation can handle huge amounts of data. In the past, only a handful of organizations were able to store large amounts of data. There are now large-scale parallel processing options and high-performance storage technology available both on-premises as well as in the cloud.

You cannot store everything. You need to be able to handle different types of data that are coming into your big data analytics. These data lakes are also known as data repositories. A data lake, like a real lake, is fed by multiple streams. It contains many species of fish, plants, and other animals.

A data lake shouldn't be used as a dumpsite. You should be careful about how data is combined to create more meaningful attributes. A data lake should be created where data ingestion, indexing, and normalization are well-planned elements of a big data strategy. Your data-intensive projects will fail if your blueprint is unclear or poorly understood.

You need to have enough bandwidth. Without sufficient bandwidth, data will not move quickly enough from different sources to the data lake. You need fast disks capable to handle millions of IOPS if you are going to fulfill your promise to offer massive data resources.

Speed is essential for real-time analytics, such as traffic routing and social media trends. When creating your data lake, make sure you have the fastest connection.

Make sure security is considered in every aspect of big data analytics

Client information is the majority of data that is collected, stored, analyzed, and shared. Some of this data can be identifiable and personal. If such information is not protected, it could lead to unhappy clients, brand damage, and even loss of money due to regulatory fines or lawsuits.

You should establish basic enterprise tools, use data encryption whenever possible, and manage access to the network. Your security measures should include training on data access and policy enforcement.

Prioritize quality and data management

Make sure your big data management platform project succeeds by ensuring you have high-quality data management. If you fail to do so, your chances of project failure will increase. You must establish controls that promote accuracy and ensure data is up-to-date, timely delivered, and that they are maintained.

The chances are you will fail, and fail big. But I'm here to show you how to fail big. If you are going to fail, here are 8 ways to get the job done.

1) You can't explain the genius

Want to fail at epic proportions? Learn every new BI buzzword, such as predictive and prescriptive analytics, cloud computing, streaming analytics, NoSQL Dbs, Hadoop, and so much more. Put those terms into a business case and randomly ask for a ton of cash. Don't worry about trying to explain it, since people will be so intimidated by the verbiage that they won't even ask. Bingo-- here comes a weekend in the Bahamas!

2) Start Big, no HUGE! -- Get a big budget and go for it

Big money, bigger whammy! To piggyback on the previous tip, don't go small with your budget. Instead, go huge with a ton of promises, and ask for a ton of cash to fund it. There is a saying that "the bigger, the better," and the more arbitrary something is, the harder it is to know if you didn't do it. Make it huge--and long-- so something like a 10-year project should do the trick.

3) You already know it all-- don't ask a customer, don't listen

You know what you need, so don't worry about asking the customer. I mean, sure, you can meet with them to make them feel better, but let's be real-- you've been in IT for 30 years, so you know what's going on. You don't have to ask anything because you most likely already know what they need before they even think to ask. So, march forward with that knowledge.

4) Boil the ocean YOLO, get it done in one fail swoop, emphasis on fail

Don't concern yourself with small potatoes, since data integration is all the same. Therefore it doesn't matter if you're converting one source or ten sources; try to knock them all out at once. Get it done fast! Bring all the sources you can think of at once. While you are at it, change the presentation platform to a new piece of software. Change is good, right? After all, consider it like doing surgery-- it is best to fix everything while you have the patient cut open.

5) Just keep going (even if it looks bad)

March forward even when it looks like you may be on the wrong path. "Don't stop, get it, get it" should be your mantra. Ain't nothing gonna stop your stride; you've got to keep on movin'. If it looks like that data is bad, don't worry about it. Report on it anyway. You've got deadlines-- the quality of the data isn't your issue. Instead, it should be the businesses. They should take better control of what they are doing. You just lay the pipes-- you're not concerned about the quality of the water.

6) Planning is for schmucks; you'll figure it out later

Don't concern yourself with lengthy planning meetings. BORING! And who has time for that? You are smart enough to get it done as you go. You'll figure it out. After all, that's what all that education is for-- you even figured out IT when your degree was in criminal justice! You got this!

Conclusion

If you want to show immediate benefits to your business, you must demonstrate that project's value to you, your managers, and your end-users. Contact our IT specialists or data scientists at PerfectionGeeks big data consulting company if you're looking for big data analytics solutions that work.