Data gathered by businesses is a minefield of opportunity. The insight from this data can offer customer insights, identify problems within an organisation, and if leveraged sufficiently will enable growth and prosperity.
Since 2009, the amount of data gathered by businesses have boomed and will continue to do so. Data production was 44 times greater in 2020 than it was just over a decade ago. Of course, data of this volume has the potential to transform the fortunes of many companies, but it also presents a series of challenges many companies are ill-equipped to face.
This 2 part blog mini-series will highlight the issues that businesses may encounter when handling large data sets, and the solutions to fix them.
Part 1. The challenges associated with Big Data.
The specific problems a business may encounter when dealing with large data sets will be determined by the type of industry you are working within, the type of data you are dealing with, and the company infrastructure. However, there are some common problems that recur when handling big data.
You can’t find the data you need.
One common challenge of big data analysis is the sheer size of it. The breadth of data means that there is data that covers every variable. Customer interests, website visits, churn rates, conversion rates, and financial data are just a few of the categorisations that the data can address.
While a lot of this data will be relevant, there are massive amounts that won’t be applicable to your business and its aims. It can be hard to identify which sets are valuable and which are not. This problem can present itself if the data coming into the business is unfiltered and structured.
The data is inaccurate or outdated.
Sometimes, less is more. If you have too much data in your databases, then it’s likely that at some of the data is no longer valid, or that it is inaccurate.
If this is the case, it’s likely that this error occurred during the collection process of the data lifecycle. This issue becomes more prevalent if the data is collected from several sources. For the data to be insightful, its collection needs to be standardised across all channels. If it isn’t, the data team can run into problems when trying to analyse it.
Should the data be collected across many different channels, there may be problems with the communication between them. If these don’t “talk” to each other and are studied by different teams there is a danger that there won’t be a big picture mentality.
The upshot is that poor practices at the point of data collection occur then this will lead to low levels of accuracy, validity, and security. If you can’t rely on the data itself, you can’t trust the analysis gathered from it.
The data is stored in silos.
Data Silos are another issue that can trip you up when dealing with big data.
Should all the data be stored in separate databases that don’t communicate with each other, the data is siloed.
Independent storage of the data means that teams aren’t all looking at the entire data; more chapters that don’t tell the whole story. Should your data team only be able to see a portion of the data, it can lead to incomplete business decisions. For example, are your marketing efforts and sales misaligned? Perhaps this is the result of a miscommunication between your marketing and sales data. Are you experiencing high levels of customer dissatisfaction? In this scenario, this could be because your customer service department is misinterpreting customer needs based upon the data analysis it’s receiving.
Data needs to be looked at through a 360 lens so that a company can extract the best value from the data.
Data security and protection are not prioritised.
A greater volume of data offers greater opportunities for security breaches. As businesses grow, they inevitably add new tools, software, and technologies. While these all add value, they increase the likelihood of security lapses.
Here are some of the potential threats to your data security.
- Fake data generation.
If you are gathering data from multiple sources, you may be inadvertently harvesting fake data. This data is potentially harmful and inaccurate. Data of this nature will affect any analysis you can get from it.
- Unsecured data sources.
Data collected from un-secure channels results in systems that are open to external infiltration and even malware.
- Stored data that is unprotected.
If you are storing data that has been collected without adequate safeguards, such as firewalls, encryption and access control, this data becomes vulnerable. Issues you may encounter will be leaks, malware, and data harvesting. This triad of factors will be damaging to your business and will compromise the security of your customers.
- Non Compliance laws.
If you don’t have a strategy to ensure compliance with data protection laws, there’s a much higher risk of exposure. Without methodologies in place to track and standardise all the channels through which you gather data, it’s impossible to guarantee that users are providing appropriate consent.
Are you looking for cyber security talent to help protect your business from an unwelcome and dangerous cyber attack? Here are our thoughts on the Cyber Security professionals that your organisation needs.
The shortfall of big data talent.
More and more, businesses are finding it increasingly difficult to source the talent who are equipped to organise, manage, and analyse big data.
The tech and tools around big data are evolving rapidly. As a result there aren’t an abundance of professionals who can use it at an expert level. It goes without saying, that if your team doesn’t have the skills and know-how to collect, manage, and build actionable reports from big data, it’s almost impossible to strategise your business activity.
Next time…
In part 2, we’ll look at how businesses can address these issues and formulate an effective big data strategy.
Do you believe you need to build your data team to help you address the big data challenges within your organisation?
We can help. Our well-connected recruitment partners have an extensive network of data professionals. So whether you are looking for data analysts, data engineers, data scientists, or business analysts, we are already in touch with your next hire.
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