“I can’t find my high school yearbook. No, I don’t know which box it was stored in, and I don’t know where that box is, and I don’t remember what else was in that box.” Sound familiar? Organizing our “stuff” has always been a challenge — which may explain why Marie Kondo has become a household name in the art of cleaning up and organizing one’s personal effects.
Believe it or not, businesses face a similar challenge in managing company data, albeit on a much grander scale and with far more complex requirements. Over the years, most large enterprises have accumulated several petabytes of dark data — web logs, old emails, and out of date customer profiles all collected in the regular course of business that will probably never be used again — with each petabyte being equivalent to 20 million completely filled four-drawer filing cabinets. As data growth continues to accelerate, the regulatory and legal requirements to control this expanding digital mountain keep escalating, causing data cleanup to rise to the top of business priorities.
Notwithstanding the chasm between cleaning up at home versus the enterprise, here’s how we can apply Kondo’s six rules of tidying up to the enterprise.
Commit to tidying up
When it comes to cleaning up, upfront commitment is important. Without it, efforts are likely to founder and peter out. A successful data cleanup initiative requires commitment from top management, including assigning the appropriate resources and budget. The formation of an “Information Governance” committee, comprised of various stakeholders representing legal, compliance, records management, privacy, security, and various business departments, can help to ensure the long-term success of the project. Getting buy-in from each of these groups would have been an uphill battle in the past; however, new data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA) have raised the stakes for management, especially in light of U.S. class-action litigation risks in the event of privacy breaches.
Imagine your (company’s) ideal lifestyle
Kondo suggests setting clear goals — without them, tidying up may quickly lose meaning. Cleaning up your company’s data is no different. To wit, set target goals; define processes and follow them; measure progress regularly; and stay the course. Furthermore, it’s essential to have a long-term plan for continuity after the initial cleanup process. Otherwise, your data will quickly revert to its natural state of chaos. This often means putting in place automatic policies so that data is continuously cleaned up as it’s created.
Finish discarding first
Kondo advocates a strong focus on getting rid of unneeded items. It’s a helpful standard — appropriate deletion of data should be viewed as a good thing. For instance, deleting data before moving to the cloud makes a lot of sense, since there’s no point wasting money and time moving the “junk.” In fact, Gartner research estimates that up to 85% of enterprise data is ROT (redundant, outdated, trivial), much of which can be identified with an initial analysis.
However, at an enterprise level, deletion must be defensible. The enterprise must set a pre-defined policy for data retention and deletion, ensuring that it complies with privacy requirements, regulations, and records policies, while also checking whether it’s part of ongoing litigation or anticipatory preservation.
Tidy by category, not by location
Kondo advocates tidying up by item type such as clothing, versus tidying up by room. Similar rules apply in the corporate world, especially when dealing with globalized IT platforms. If offices worldwide use the same data platform, let’s say for email, then it is better to handle all emails across the globe at once than to tidy up all types of information at once, country by country.
Follow the right order
Kondo suggest starting with the easiest category of item to make a decision — which in the home is clothes, she says. In the enterprise context, this approach makes a lot of sense. One should start first with easier data types, such as internal file drives or SharePoint. From there, you can tackle email, instant messaging, and social media data, and then proceed to the more complex environments such as cloud apps, logs, ERP data, and machine data.
Ask yourself if it “sparks joy”
While it’s obviously not recommended to use emotion as a measuring stick for keeping data, the concept of sparking joy can actually be reflected nicely in the corporate context — as long as you define “joy” as superior corporate performance.
Analytics is a top priority for companies; however, up to 73% of data goes unused for analytics, often because it’s unmanaged. In fact, one of the most useful types of analytics remains unharnessed: analytics based on textual data createdbyhumansforhumans, such as email and file shares. Cleaning and managing this data can drive more effective analytics and offer insight into the human side of the business. For top management, this certainly could “spark joy.”
Scaling Marie Kondo to today’s enterprise
Marie Kondo’s principles of tidying up can help to establish a surprisingly solid framework for cleaning company data. With that in mind, IT leaders and data management teams should beware the differences in scale and complexity in the enterprise, where even simple tasks can become a challenge. Companies must satisfy complex governance obligations simultaneously; therefore all actions taken on data must be orchestrated or unified across the organization.
Technology is necessary to augment human effort. Due to the sheer volume of data, it is simply not possible for humans to individually categorize and apply management to each document. This is where technology steps in with analytics, artificial intelligence, and machine-learning, to automate the data categorization and lifecycle management.
Now, if data management technology could only do the same for physical sweaters and yearbooks, Marie Kondo would have reason to worry.