Hackernoon logo5 Data Management Principles That Matter in 2021 by@winpure

5 Data Management Principles That Matter in 2021

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Global leader in data cleansing solutions.

Data is constantly growing, building, and piling up. By this time next year, research suggests that around 93% of all data may be ‘dark’. Dark data, for the uninitiated, is that which many people disregard. It is non-essential data that churns out day after day - Facebook posts, video data, and old spreadsheets.

This statistic is particularly concerning in 2021. Efficient data management has never been more vital, and not just for the fact that the population is ever-growing. In an age where consumers demand increasing efficiency re data access, it’s essential to know how to handle information.

Thus, it is crucial to understand - and practice - some of the most effective data management principles. Data management is less about filing information and knowing where to find it. It is about cleaning data silos, removing duplicates, and establishing clear, efficient order.

Let’s consider a few fundamental data management principles for 2021 that genuinely matter.

1. Plan for Data Early

A key problem many companies face when it comes to dirty or dark data is clearing up one’s mess. It is easy to assume that cleaning data is simple enough after the event. However, some firms require large-scale restructuring and silo breakdowns to get up to speed.

Ultimately, the phrase ‘prevention is better than the cure’ applies to data management. Data managers and scientists agree that creating a plan for big data from the outset is hugely beneficial. For new businesses, this means crafting plans from the get-go. For existing brands, this may mean wiping the slate clean, to an extent, and re-learning.

From the start of a new data management strategy, scientists and managers need to consider specific aspects. They need to consider which data is useful, where it has come from, and how it will be stored.

Moreover, data managers must have a plan in place to establish data quality from here on out. This is not difficult to learn with the assistance of a data management or cleaning platform.

Ultimately, businesses need to establish early on which sources are unlikely to provide trustworthy results. It is vital to establish a ‘key’ as to which data silos or bases should build towards a ‘clean’ new standard. It is tempting to keep things as-is, but if there are data sources only adding to cleanliness problems, it is time to remove them.

2. Understand Your Own Meaning of Data Quality

We can all agree that quality data is that which delivers value. This is data that is easy to manage, which up to date information and actionable numbers. However, businesses must establish their own meaning of quality data from the outset.

These standards may vary, even within a business, depending on projects. However, there needs to be a clear definition as to what ‘quality’ means. To establish this, we highly recommend running preliminary tests. When handling data regularly, which information proves helpful, and which may be a waste of resources?

Are there any areas where duplication occurs regularly? Do you notice trends in inaccuracy or outdated information in specific cases?

To achieve proper data cleanliness and manage data effectively in the modern age, we need to establish the rules. Data cleaning services can help us to set our own bespoke parameters for cleanliness.

There are always going to be some universal measures. Duplicate data, for example, is almost always considered ‘dirty’ data. As such, from 2021 onwards, businesses must pay attention to their own templates and standards. Data is complex in itself, but the way we handle it, variously, can help to make matters all the more confusing.

3. Set Up Data Life Cycles

On paper, data has a reasonably simple life cycle. It is created, then eventually archived or deleted. What happens to this data in between? Do all of your data travel through the same processes and systems?

Setting up an effective data management plan in 2021 relies on the clarity of your data life cycle. Ultimately, to keep data clean, we cannot simply file it in a new and efficient way. Much as we explore in the point above, we need clear plans of action, clear quality markers.

Life cycles are great data markers that are very easy to understand. Of course, most businesses will establish the creation and deletion ends of the scale. However, in between, you may store data a certain way. You will, in some cases, share such data with third parties or elsewhere in your business.

By establishing a clear life cycle that applies to all data travelling through your business, it becomes easier to understand its journey. This also helps us to understand the quality and usefulness of our data with more clarity.

4. Don’t Overlook Metadata

Metadata is evolving. Metadata ties in nicely with our above point. At the simplest level, this type of data maps out where information travels, how it is stored, and if any changes occur. Metadata is a vital component to establishing any kind of life cycle or overall quality plan.

Data scientists believe that a new revolution concerning metadata management is only around the corner. Some refer to this as ‘Metadata 3.0’, whereby it will become easier for us to manage information at this level with modern platforms.

For existing business managers, this simply means it is worthwhile waking up to metadata. It is crucial to ensure you have a plan in place to process these numbers. Without information at meta level, we are at risk of all data converging into one mass. It becomes challenging to distinguish datasets and individual strands.

Therefore, we must keep digging deep into our data. Metadata will help ensure higher quality collection and support compliance, tracking, and collaboration.

5. Policymaking is Essential

The above points are all important; however, they all risk falling down without an attitude that pivots towards policymaking. With data ever-expanding, a business without clear data policies risks letting down consumers and missing compliance targets.

Inconsistent data, and data laking, often arise through a lack of documentation or roadmap. We could apply the principle of life cycling in much the same way. It is all too simple for businesses to swallow data and to let it build or fester.

A business with clear policies for data clearly understands its importance and its impact on consumers. Transparent governance will help with the management of current information and prepare for future intake. Data is never going away, and with artificial intelligence ever-growing in the business world, it’s going to get more complex.

This stage in proceedings goes against what many would refer to as ‘sticking your head in the sand’. It is no longer wise to simply ignore or let data propagate on its own. Even if you do not have a clear data policy or strategy in place already, it is never too late to draft up.

The data we collect is not only going to increase in volume and complexity but also type. With the evolution of the Internet of Things (IoT) occurring before our eyes, data diversity is only inevitable. The data we collect now will likely still be around in years to come. However, it will have new neighbours; perhaps in shapes and forms, we are unable to grasp.

Therefore, making clear policies ensures, at least, your business has some form of future roadmap to work from.

Will Data Cleanliness Always Matter?

As long as data grows in complexity and volume, cleanliness will be of paramount importance.

The way we use data may not change too much over the short term. However, as mentioned, data will always build up, and information parameters will change over time. Research shows that IoT and AI will change the way for data management for the years to come. However, unless businesses start to establish clear roadmaps now, data silos will begin tipping over.

Duplicate data, and outdated data, are only the beginning. These are just two elements of dirty data that we are fighting against year after year. Just because we don’t necessarily see data at surface level, day in day out, doesn’t mean it is absent. Far from it - the concept of ‘dark data’ is very important - as some information is, at present, destined to drift away.

The idea of all data being manageable is a brave one, but not impossible. With only 7% of data avoiding the ‘dark’ tag by 2022, there is a lot of work to do. Arguably, AI and machine learning may help us to manage data more clearly in the decades to come. 

In the meantime, it is essential all business owners - and data managers - create clear policies. While predicting the future is beyond question, we can look after our data in the short term. Data management will evolve in exciting ways soon enough - and it is high time we all prepared for what’s around the corner.


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