Embracing big data and analytics strategy can also bring confusion. How? 

Data is an essential asset in modern-day business, irrespective of a large MNCs or a small, locally owned business. If an organization doesn’t have an effective data strategy in place, it will miss out on the huge potential business value that data offers. Big data & analytics can be a promising investment for enterprises as it allows them to glean and process large datasets to derive valuable insights.

However, leveraging data can also bring confusion and deter businesses from realizing their full potential. So, how to avoid this confusion?  By avoiding big data mistakes at the initial stage itself. 

Before getting started into data projects, businesses must consider these 10 big data mistakes and should avoid them: 

1. Relying on data warehousing for resolving issues

Data warehousing can prove to be useful for a myriad of technical challenges but is not potent for solving all kinds of big data issues. Warehouses aren’t ideal for video, text, and images, and their application should always be restricted to customer-centric structured information from a handful of data sources. 

2. Relying on the same KPIs

Keeping with ever-changing environments, businesses are required to adapt themselves to advanced strategies and solutions. Most companies still use conventional key performance indicators that may hold them back from those that are exploring new tools and technologies. 

Hence, to prosper in today’s fast-moving digital world, organizations need to use novel and more suitable tools to make advanced data analytics tools that reflect the current performance of the business and identify what really drives a business forward

3. Not using a workflow-management tool

Businesses spend large amounts of money producing analytics on big data to make business decisions. knowing what to do is only the first part of the equation. Acting on that data with a workflow-management tool is critical to success. Companies should put equal value on big data analytics and solutions that can automatically act on the results. 

4. Not planning for AI/ML to be disruptive

Make no mistake: AI will displace some of your workers and has the potential to upend how you handle your operations. But there is only one choice: you can be a disruptor, or you can be disrupted. 

If you want to lead, you must be willing to pay for talent and act quickly because the best talent is being snapped up fast. HR won’t like what you need to pay for machine learning (ML) experts but spending money now on experts nets you a much greater return in the long run. And, don’t make the mistake of contracting this essential skill out.

5. Lack of data security concerns

Security and governance are the major crucial business concerns. While organizations are started leaning towards big data analytics projects, they are moving towards it without security and governance baked in. In this way, they need to consider a multifaceted approach for securing big data. 

This should involve an understanding of the data possessed, auditing the manipulations of data, and holding control over the privileged users. In addition, they must comprise compliance, governance, and security conversations that started at the beginning of the project.

6. Paralysis of analysis

It seems the ‘look before you leap’ policy is still unknown to many businesses who jump into the initiatives of Big data with colossal data collection. Stalled projects and paralysis of analysis are sure-shot consequences of issues in big data analytics. 

7. Belief in ‘traditional’ techniques

Traditional data integration isn’t going to cut it in the world of big data. The two most common ones, extract, transform, load (ETL), and master data management (MDM) processes, are too old to work properly and won’t scale.

For your first foray into the world of big data, start with a small, well-defined initiative. Your data should clearly support your hypothesis or refute it. If your data produces ambiguous results, keep paring it down. 

8.  Not moving to the cloud

If your organization isn’t planning to become cloud-exclusive, you could be backing losing technology. The cloud is more elastic than your in-house solution and more cost-effective in the long run.

The cloud will save your organization a raft of money, allow your business to take advantage of new technologies with elastic compute, and open your organization to new geographies. Take action now and look into what the cloud offers. 

9. Not appointing a ‘Data Czar’

Every company complains about data quality and accuracy. The mistake is that they often don’t have central oversight on how to collect data, so they end up with duplication, columns being used incorrectly, or just bad input. 

Make sure that there is a role (or committee) responsible for data hygiene in your organization and give them the mandate to keep it clean and train your users. 

10. Overlooking external data

Today, data comes from various sources and in many more forms than just databases and spreadsheets. Most data businesses collect are unstructured or raw data such as photos, sound recordings, text files, and so on. Thus, having a robust data strategy needs to account for structured and unstructured data that can excerpt meaningful insights. 

However, overlooking external data sources including data repositories, governments, and data brokers can put businesses’ data projects at standstill. Businesses must deliberate every type of source of data that can deliver value for the business. 

To Sum Up

Leveraging big data is a challenge for businesses, but it helps to be aware of some common mistakes. 

With so much complexity around big data, businesses can easily make mistakes when working with their data sets. Refraining from the above-listed areas will somehow help your company avoid these big data pitfalls. The secret lies in the capability to collect, sort through, and collate data from diverse sources. This brings in the capability to increase the insight-level and make data-based decisions that enhance business enablement. 

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