3 Data Quality Issues to Be Aware Of
Updated: May 30
Low quality of data is a concern for any organisation. The more problems you can prevent with data quality, the better.
Whatever sector you work in, you undoubtedly have heard a million times how essential information is nowadays for organisations. Even people call data the century's "new oil.” However, data management costs are expensive—and this cost will surely rise with your firm. Therefore, it is very crucial to avoid data quality problems. The costs of inadequate data are frequently larger and more unexpected than anybody expects, so it's always a great benefit to proactively address data quality.
Here are three things to consider:
Data is Often Squandered and Misused
Most businesses nowadays gather data and use it to influence decisions in some way. That is just a competitive norm nowadays. However, the majority of businesses still do not have a data strategy in place, which might result in a lot of missed opportunities. One of the best aspects of data is that it can be filtered and reused. A simple example: the raw data used by the marketing department to track KPIs may also be utilised to track a product's financial performance. This may appear to be plain sense, but far too many businesses overlook simple things like this. You really need a data strategy if you are not aware of who is utilising what data and how it is used inside your organisation. If you want to start treating data as what it actually is, having a data strategy is a must: an asset. It's preferable to constantly guard and maintain it, just like any other asset.
All Data Models Have Their Limits
Although your organisation's current data model may be commendable, you should know that no data model survives forever. There will always be a certain level of data that will outstrip the capability of your existing data model, introducing several inefficiencies. In other words, as data volume grows, most data models will begin to fail. This isn't always the result of a bad data model; it's just the way things are right now. All you can do is face the truth: most businesses will encounter data quality issues as they develop, and these issues will not become evident until the data volume exceeds a certain level. To further complicate things, we're still trying to cope with the pandemic's high levels of uncertainty. We must be especially prepared to contend with issues in our data models in the coming months. Consumer, business, and market behaviour will all change, posing a slew of new difficulties for current data models. Prepare to rescale your data in the middle of the project.
The Scale of Data Anomalies
One of the major problems faced by data scientists throughout this decade was finding logical patterns in large datasets. We can all agree today that data does not always follow logical patterns, and what is known as anomalies is one of the primary causes for it.
Abnormalities (or transient fluctuations) always appear in data patterns to keep us sharp and moving. And, no, we don't speak here about the seasonal changes owing to famous occasions such as Christmas and Independence Day. We discuss of apparently irregular and short-lived patterns which endanger the decision-making process.
Manual inspection and adjustment for outliers is achievable in the earlier stages of development. However, as your firm develops and accumulates more data, you start to run into new anomalies more frequently, and you will require more sophisticated tools. Today, most organisations employ custom-designed algorithms to help them automate the most challenging activities in this process through software development services. Getting strong technology at your side is undoubtedly a fantastic approach to avoid abnormalities in your choices.
If there's any important takeaway from this blog post, it's that an appropriate data strategy is absolutely vital. The way companies handle and exchange information throughout an organisation has evolved, and each leader has to understand how their data may be used to the greatest advantage of the enterprise. Moreover, the aforesaid difficulties will assist you realise your method is scalable and repeatable.