Don’t Leave Your Data in the Dust – It’s Spring Cleaning Season

It’s spring-cleaning season, meaning many diligent homeowners are busy trying to organize their closets and homes in preparation for the spring and summer. While this annual binge of tossing out unwanted junk and meticulously cleaning and sorting the rest of what we’d like to keep is a ritual for many of us, it’s no longer synonymous with only being a household chore.

Organizations, both small and large, also must keep up with cleaning their virtual dust bunnies on a regular basis, just as one would do within their own home. As businesses of all sizes are collecting an ever-increasing amount of data, it turns out there are many similarities between spring cleaning and the steps individuals managing their organizations’ data must take in order to ensure their data is clean, organized, and consistently updated.

Like cleaning a house, data cleansing and organizing can be overwhelming, but it doesn’t have to be. Following the below tips will help organizations keep on track and will make the task of maintaining clean data less daunting, while also improving efficiencies within their business.

Know where to start: How to sort data  

When cleaning a household, homeowners typically look inside each of their rooms and closets to determine what areas need the most work before they start to actually clean. Similarly, individuals managing their organization’s data should first do data profiling and gap analysis to identify where the biggest needs are for clean-up around their data sources, databases, and business initiatives.

Data profiling is the process of examining the data available from an existing information source (e.g., a database or a file) and collecting statistics or information summaries about the data. The purpose of these statistics may be to find out whether existing data can be easily used for other purposes. A gap analysis is a method of assessing the differences in performance between a business’ information systems or software applications to determine whether business requirements are being met and, if not, what steps should be taken to ensure they’re met successfully.

Moreover, data profiling helps organizations understand how the quality of their data impacts their business processes to enhance operational, analytical, and data governance initiatives. It also provides an understanding of whether the source data is still fit for internal systems. A survey of 80 data scientists found that they spend 60% of their time cleaning and organizing data, which reflects how necessary data profiling is for organizations. Businesses need timely access to trustworthy data and insights so they can use it to streamline processes and maximize efficiency.

How to Clean, Organize and Store the Data Properly

The goal of spring cleaning is typically to declutter the home and get rid of unnecessary things such as outdated clothing, shoes, and accessories. Sometimes decluttering the home requires people to donate their clothes, throw them away, or store them for next season. For organizations cleaning data, an important step to take is to establish a governance and data lifecycle management plan to make sure the information is shared with others in the organization who may find it useful.

Data life-cycle management is an approach to managing the flow of an information system’s data through a life cycle: from creation and initial storage to the time when it becomes obsolete and deleted. It’s important that individuals managing data determine if the data can be recycled and restored or if the data no longer has a purpose. Essentially, data life-cycle management looks at data the same way a homeowner would look inside of their closets to determine if they need to keep their clothes in their current place or if they can be moved to the attic or to storage bins.

Oftentimes, many people don’t want to share data once they’ve cleaned it up due to causes such as organizational or line of business silos, privacy, and security. Additionally, sometimes data isn’t shared because the data that was cleaned up already served a specific project or use, and those handling the data didn’t think others could benefit from it once the data fulfilled its duty.

Meanwhile, other people within organizations create trusted data sources and then throw it out into the universe without effectively communicating to others in their organization what changes have been made and where the data can be accessed. This can cause confusion within an organization and can make businesses less efficient.

One way to overcome the challenge of disorganized data sharing is through the use of collaboration tools. Collaboration tools allow users to check in and view data quality or preparation projects. Essentially, these tools provide an open workspace where businesses can track the data changes users made, as well as who made the changes and when they were made. These tools also offer easy-to-use functions like “undo data” changes. Ultimately, collaboration tools will help people become better at sharing their data for other people to use and can provide better insight into what’s happening within an organization.

In general, it’s a best practice to use data sharing tools as much as possible. IDC predicted that through 2020, spending on self-service visual discovery and data preparation tools will grow 2.5x faster than traditional IT-controlled tools for similar functionality. Meanwhile, Gartner notes that the adoption of bimodal (the practice of managing two separate but coherent styles of work) and adaptive sourcing approaches can also optimize the cost of data quality tools. In general, this improves sourcing flexibility and drives high business value while maintaining fit-for-purpose governance.

When data is stored correctly within data warehouses and databases, organizations will find success with making their businesses more efficient. Data needs to be archived and put away like precious photos that we don’t look at every day but don’t want to get rid of because they have value.

Get in a Habit of Regularly Cleaning

Keeping data clean, updated and organized can be an overwhelming task if it’s not done regularly, so it’s best for organizations to clean on a regular basis to ensure they aren’t missing the chance for their data to be high value. Once a cleaning cycle is established, the task of cleansing, updating and organizing will be minimized and it will become a normal part of the routine.

Artificial intelligence and machine learning technologies can take on the automation of data discovery and data cataloguing within organizations – which relieves much of the burden of manually sorting – ultimately saving time and eliminating human error. As artificial intelligence and machine learning continue to bring more capabilities, organizations will need new standards to perform in increasingly complex data environments.

Machine learning is a type of data analysis technology that extracts knowledge without being explicitly programmed to do so. It provides systems the ability to automatically learn and improve from experiences and focuses on the development of computer programs that can access data and use it to learn for themselves.

More and more, data management technology will be leveraging machine learning and artificial intelligence to find data patterns or to handle the data profiling and automatically recommend the data rules or changes that users should make to the data to fit the purpose. Gartner recommends modernizing organization’s business intelligence and analytics capabilities to support machine learning by updating the data organization layer in end-to-end analytics architectures to support data preparation for machine learning algorithms. On top of that, businesses should focus on storage and compute clusters to support machine learning capabilities.

When businesses monitor their data quality consistently, they are applying their data validation rules to data sources on a regular schedule to get historical trend analysis and to see how data quality scores change over time. When businesses react early and proactively on slight decreases of given data quality scores, they’re able to keep business operations running smoothly.

Why Cleaning Matters  

Organizations that understand the value of integrating, cleansing, managing and archiving data to boost their business process and analytical insights will have an advantage over businesses who do not value such capabilities.  According to Gartner, poor data quality is a primary reason for about 40% of all business initiatives failing to achieve their targeted benefits. Supporting this argument, a survey of theirs indicates that failing to measure the annual financials of poor quality data impacts results in reactive responses to data quality issues, missed business growth opportunities, increase risks and lower ROI.

When organizations prioritize data cleansing, they can lower operational costs and maximize profits. Data cleansing can also help businesses improve the decision-making process, streamline business practices and increase productivity.

Perhaps the biggest takeaway for data cleansing is that it can help businesses achieve their goals.

By recognizing the importance of consolidation, data integrity, and central governance, organizations will transform their data into a trusted, updated resource for streamlining processes and maximizing efficiencies. 

Just like a homeowner who frequently cleans, organizes and knows exactly where all of their belongings are in their clutter-free home, organizations who have their data frequently updated, cleaned and stored properly will also find that it makes doing business much easier.