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This article was published on September 18, 2020

6 big data blunders businesses should avoid

6 big data blunders businesses should avoid

Had Hergé been into businesses, this article would have been a point of interest to him. And he could have avoided the Big Data errors in his ‘Tintin’ enterprise. 

Are you willing to follow down the line too? Not yet intrigued? #Sigh# Thought so! Let’s get you hooked!

Here’s the modern business landscape – Data data everywhere, not a drop to waste! Data has become considerably crucial for modern businesses. In this age even AI is getting powered by Big Data. 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. The leverages extend from marketing, internal workflow to sales for businesses. 

Now, where does Big Data come into the business realm? Let’s get to the root of this, shall we?

Big Data and Businesses – Where’s the Relation?

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Owing to modern technologies, all trades, irrespective of size, have access to granular and rich data that is based on their operations and clients. The major hurdle in this is dealing with a massive quantity of data that are both challenging to maintain and costly to manage. Despite the presence of appropriate tools, dealing with such data is a cumbersome activity. 

Errors are a frequent presence with the layers of complexity involved in dealing with Big Data. However, Big Data holds diverse leverages for businesses. This includes –

  1. Boosts revenue
  2. Ensures better revenue decisions
  3. Enhances client experience
  4. Helps produce smarter services and merchandise
  5. Offers more accurate business operations

Thus, Big Data becomes the defining leverage for innovative enterprises to gain an edge over their competitors. The usage of these data is sure to exceed 274.3 billion by 2022 globally with each individual generating approximately 1.7 megabytes of information per second

With such leverages in point, can you really afford to make mistakes with regard to blunders regarding Big Data? So, here are some big data blunders that businesses need to avoid to harness its full capabilities and enjoy the leverages that it brings. 

The Blundering Blunders of Big Data

While Big Data comes with highs, lows with errors in the same are not uncommon. The big data problems comprise –

  1. Inefficiency in operations
  2. Security loopholes
  3. Incorrect conclusions

-in case of errors. So, let’s say, Big Data is like credit cards – use them well and they reap benefits; use them unwisely, and the bills are humongous! Here are the listed blunders that businesses should avoid while dealing with Big Data. Read on!

Blunder 1: Paralysis of Analysis

  • Issue: 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. 
  • Solution: Start on the world of Big Data with ‘small steps’ a.k.a small data quantity. Let your collected data refute or support your hypothesis. In case of ambiguous data, pair it up!

Blunder 2: Data Security Butchered In the Name of Innovation

  • Issue: Security is the first aspect sacrificed when working with Big Data. But what about the mitigating security concerns regarding it? 
  • Solution: There needs to be a multifaceted approach for securing Big Data. This should comprise an understanding of the data possessed, auditing the manipulations of data, and holding control over the privileged users. Make sure to cover the big-data security with a holistic and unified system of processes and controls. 

Blunder 3: Vanished Data Czar

  • Issue: Complaint about data accuracy and quality are a common occurrence. However, businesses fail to take a look to the roots of this. Lacking central oversight on data collection leads to duplications, incorrect use of columns, horrifying inputs, etc. 
  • Solution: Assign a committee that holds responsibility for data hygiene for your enterprise. Make sure to compel the big data management team to dust up the data and train the users with it. 

Blunder 4: Putting Big Data Puzzle in a ‘Flash’

  • Issue: Big Data is a colossal jigsaw puzzle that if hastened to solve, it would be a huge jumble. No organization is capable enough to tackle such a huge puzzle. 
  • Solution: Work the puzzle area by area or rather piece by piece. This will make your big data challenges, a small-data one. Enterprises are then equipped enough to tackle such challenges. This definitely eases the job, right?

Blunder 5: Meditating Data in a Silo

  • Issue: Collecting and storing Bitcoins may be of advantage, but it isn’t the way out with data. So, this is a shout out to companies doing this – if you are merely collecting data and not extracting its essence, and implementing the insights, then the silo meditation won’t be of any help. Its power to enhance operation or resolve obstacles, and inform about your product road map, gets rusted.
  • Solution: Use and extract its essence in time, what else! Don’t let it meditate or go dormant!

Blunder 6: Digging a Hole in Your Pocket for Complex Tools

  • Issue: Businesses with smaller datasets are often inclined to get into big data solutions. This quick jump means considerable investment for complex tools that exerts budgetary strain for enterprises. 
  • Solution: Organizations should be hailing their data analysis to lead on with wise-decisions with Big- Data handling. However, not all issues require the use of heavyweight tools. ‘Big Data’ traditional approaches would do!

Apart from the 6 major blunders, there are also the issues of an absent workflow-management tool, out-of-focus ROI, data not being used for evolution, etc. 

Avoiding the Blunders is a Mandate!

Big Data is going to be an echo all across 2020 and beyond for all businesses, irrespective of the genre. For experts and developers, this is clearly both an opportunity and a  challenge. As data volumes increase, they will continue to migrate to the cloud and as per predictions, the global data sphere will soon reach 175 zettabytes by 2025. The increased popularity of machine learning, enhanced demand of Chief Data Officers (CDOs) and data scientists, privacy remaining a consistent concern, and actionable and fast data coming to the front will all add up to Big Data becoming a prominent presence. 

Such prosperity of Big Data will have a lot to offer for your organization! Are you willing to pass up or mess with that? We thought so!

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