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7 Biggest Data Mistakes Businesses Should Avoid Making

7 Biggest Data Mistakes Businesses Should Avoid Making

Sameer Balaganur
7 Biggest Data Mistakes Business Should Avoid Making

It seems like every company in today’s era is more or less a tech company, and yes, it has something to do with data. Part of the reason is that years ago, businesses across the world were constantly looking out for data to improve and now that it is here, these businesses are seeking out new technology to keep up with the big data that has come in. With adopting new technology and the big data, businesses are prone to make some mistakes and often it is related to the massive data that flows through.

Below we mention some of the biggest mistakes businesses can make when it comes to managing data.

Do Not Jump Into Overwhelming Data

A common mistake that businesses do is taking on data projects or initiatives with a lot of data, without realising that it might overwhelm them. What happens over time with the analysis of this overwhelming data is that the company reaches a point where the projects keep getting stalled and are unable to move forward. Another reason for the projects stopping or unnecessarily getting dragged out is because sometimes the businesses tend to over analyse every piece of data that comes through, leading to something which is called analysis paralysis.

It is better to take the data that is well-defined by the project, and in turn, avoid over analysing. The data should support the strategy and initiative taken by the business.

Collecting Inaccurate Data and Its Quality

When talking about businesses, working on inaccurate data or an unreliable data source can result in a ton of loss in money and waste valuable time. Working with inaccurate data can be avoided by first considering the goal of the analysis. Note that the business team who is analysing the data doesn’t have to prove their hypothesis or assumptions (like price testing), they just have to align their goals with the kind of analysis they want, which will make it clear in curating the data.

Another significant aspect that comes in with data collection is the quality of the data. In a business context, any unusual change by the client would require the business team to pull up a ticket, and if the data provided isn’t what was needed, it will require changes in the whole process. So, whenever there is a product in the discussion that needs frequent changes, the process can drag out for weeks despite the right charts and dashboards.

Businesses can counter these problems of data quality and collection by opening up or creating portals that are first, easy to navigate, which makes it more accessible to non-analysts. Second, make sure that there is a shared space where there are only ‘analysis-related’ discussions happening. Third, try to introduce machine learning solutions with data.

Matching The Infrastructure With Resource

Business companies have a great influx of data, so they need to have the appropriate infrastructure in place for security and access. But, with more and more businesses moving towards cloud and SaaS businesses, they don’t need to make the mistakes of investing a massive amount into the implementation of infrastructure.

Some companies do choose to build their infrastructure for big data analytics, which again requires them to have the expertise and try to replicate the best-in-class practices.

Outdated Business Data Strategy

The business strategies have to be relevant to the industrial revolution going on with the fast-changing world. Anything which isn’t related to the 4th Industrial Revolution is deemed ineffective. And it is obvious that the business has to be in touch with the changing industry and spending time on anything else it won’t make much sense. Time and money are almost the same when it comes to businesses, so wasting any one of them won’t be a viable option.

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Taking in Different Types of Costs Into Account

Many times businesses make the mistake of taking in the technical cost alone and not a lot of budget for items that are outside of it. There are high costs that need to be accounted for before the start of a data project. Firms must budget things like skills development and training the staff as these areas contribute towards the growth of a business in a significant way.

Not Hiring A Dedicated Business Intelligence Team

When one has collected the right data, there is still a need to put it to good use. This is one of the most common areas where certain businesses struggle and make mistakes, hiring a dedicated business intelligence team.

This team needs to be thorough with the data and should be able to ensure efficient analysis and sharing data to drive the progress.

Not Considering Privacy And Ethical Issues

For any business strategy, customer/client trust is one of the focal points. Customers must feel safe with providing their data, and it can only happen if importance is given to determining access to data, ethics, security and privacy by the firms.

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