5 Types of Costly Data Waste and How to Avoid Them

Do you already know somebody who purchased rather a lot of fancy train tools however doesn’t use it? Turns out, train tools doesn’t present many advantages when it goes unused.

The similar precept applies to getting worth from knowledge. Organizations might purchase rather a lot of knowledge, however they aren’t getting a lot worth from it. This is a widespread concern that cuts throughout completely different sectors. It is estimated that almost 75% of the info that enterprises gather stays unused, and thus, the worth just isn’t realized. So, what’s the drawback?

In the health instance, the issue is often not the train tools; it’s a problem with the consumer’s habits.  Similarly, getting worth from knowledge usually just isn’t an issue with the info itself. Rather, issues come up from limitations imposed by knowledge infrastructure and knowledge practices that block efficient and environment friendly use. In different phrases, poor selections in knowledge infrastructure and knowledge habits can lead to knowledge waste. 

What is knowledge waste, and why does it occur?

Fundamentally, knowledge waste means lacking a possibility to get worth from knowledge or paying an excessive amount of to purchase, retailer, and use knowledge. In large-scale programs, knowledge waste is available in many kinds. Some are stunning, most are costly, and nearly all are avoidable. 

To keep away from pointless knowledge waste in your group, first you will need to acknowledge it. The following describes 5 frequent ways in which waste happens:

• Data is used and then thrown away

A standard knowledge behavior that ends in missed alternative is assuming knowledge has no additional worth as soon as it’s been used for the actual function. Data is ingested, processed, reworked (maybe for a particular report or to be saved in a standard database), and then the uncooked or partially processed knowledge is discarded. It isn’t sensible to save all of your knowledge, however it can be crucial to understand knowledge could also be beneficial for different initiatives. You lose that add-on worth while you throw knowledge away. 

This kind of knowledge waste ends in lacking out on the second undertaking benefit. For instance, AI and machine studying initiatives provide nice potential worth, however they’re speculative. Lowering the entry price by re-using knowledge and infrastructure already in place for different initiatives makes making an attempt many various approaches possible. That, in flip, makes it extra seemingly to discover those that repay. Fortunately, learning-based initiatives sometimes use knowledge collected for different functions. 

It’s additionally necessary to return to uncooked knowledge to ask new questions and practice new fashions, significantly because the world is consistently altering. Features that you just didn’t assume had been beneficial at first might later be simply what you want. You’ve misplaced that chance if the info has been thrown away.  

• You have knowledge however don’t use it

Why does beneficial knowledge so usually go unused? One motive is individuals don’t know the place it’s and even probably that it exists in any respect. Lack of annotation with the proper metadata is a contributing issue. Another is poor communication between initiatives or enterprise models.

An even bigger concern is that individuals might not understand how to see worth in knowledge. Recognizing what knowledge can inform you is an acquired talent for individuals past simply knowledge scientists. New approaches are being developed to perceive and use unstructured knowledge, as an example. But to get the advantages knowledge has to provide, you will need to be taught to use it, similar to you want to understand how to use train tools earlier than it may possibly do you any good. 

Another issue that retains individuals from totally utilizing and re-using knowledge is knowledge infrastructure requiring specialised instruments. This limitation makes it inconvenient for knowledge to be utilized by differing types of functions or completely different analytics and AI instruments. Increasingly, individuals search for methods to unify their knowledge layer and have versatile entry so as to construct a data-first setting.

• You have knowledge however not the place it’s wanted

Data within the fallacious place is about the identical as knowledge that doesn’t exist. And “fallacious place” can imply multiple factor. It could also be that knowledge is held by a unique enterprise unit, making it troublesome to establish or difficult to get the permissions and entry wanted to share that knowledge. Once once more, there’s a price for not utilizing knowledge as a result of it’s someplace apart from you’d prefer it to be. 

Another means knowledge is within the fallacious place is in a extra literal sense: geolocation. For giant programs, main knowledge movement from edge to knowledge middle or between knowledge facilities which might be situated in numerous cities or nations is difficult, particularly for those who shouldn’t have knowledge infrastructure designed to do transfer knowledge  mechanically. Coding knowledge movement into functions just isn’t an satisfactory different besides within the easiest of circumstances. To keep away from knowledge waste, you will need to have a means to effectively transfer knowledge to the place it’s wanted. Otherwise, hand-coding of knowledge movement can lead to further issues, together with undesirable duplication.

• Your system includes undesirable duplication

Having pointless duplication of giant knowledge units is clearly a waste of the sources used to retailer and entry knowledge, but it surely includes waste in different methods as properly. Duplication of knowledge additionally entails duplication of effort, which is an extra price. And the issue isn’t just a matter of too many copies of knowledge. (*5*) duplicated knowledge units might introduce uncertainty about knowledge high quality. Near duplicates instantly elevate the query of which is authoritative and why there are variations, and that leads to distrust about knowledge high quality. 

Hand-coded knowledge movement by many various customers creates its personal issues, as that is onerous to do precisely at scale. Resultant knowledge units can introduce unintentional variation in knowledge even the place a verbatim copy is meant. 

Another associated drawback is the creation of knowledge silos in giant programs. Unwillingness to share knowledge usually factors to the dearth of a uniform knowledge layer with flexibility in knowledge entry. Siloed knowledge not solely ends in avoidable prices, but it surely additionally limits the understanding and insights knowledge scientists and analysts can draw from the info. Siloing and poor knowledge discovery capabilities are wasteful via alternative price plus the fee of redundant storage and duplicated effort.

A particular instance of knowledge waste via pointless duplication happens when an enterprise buys knowledge that would have been obtained totally free. This waste occurs as a result of individuals might not know what knowledge choices can be found.

• Disconnect between knowledge producers and knowledge customers

One drawback with connecting knowledge producers and knowledge customers is that those that produce knowledge and even these accountable for knowledge ingestion usually have no idea how it will likely be used. That disconnect makes it more durable for individuals who want knowledge to know the place to discover it or to know what the info really consists of after they do discover it. Data producers are challenged with annotating knowledge appropriately with out understanding the methods it will likely be used. This disconnect between knowledge producers and knowledge customers leads to a basic kind of knowledge waste within the sense of missed alternative or pointless effort and expense required to observe down knowledge.  

Reducing knowledge waste

How are you able to tackle the problems listed above so as to cut back knowledge waste? You want to develop a complete knowledge technique that features a unifying knowledge infrastructure engineered to assist versatile knowledge entry, knowledge sharing, and environment friendly knowledge movement. HPE Ezmeral Data Fabric is a software-defined and {hardware} agnostic knowledge expertise used to retailer, handle, and transfer knowledge at scale throughout an enterprise — from edge to knowledge middle, on premises, or within the cloud. As such, it serves as a unifying knowledge layer that helps a variety of functions and instruments, thus inviting the re-use of knowledge. In addition, knowledge material handles knowledge movement mechanically at a platform degree. 

Other options come within the type of higher use of metadata to help in knowledge discovery and understanding, together with new knowledge initiatives to higher join knowledge producers with knowledge customers. One new initiative is the Agstack Foundation, an open-source digital infrastructure for agriculture. Another instance is Dataspaces, a brand new service platform that helps knowledge producers and knowledge customers combine numerous knowledge units, improve knowledge discovery, and entry and enhance knowledge governance and belief.

These options may also help you cut back pricey knowledge waste and take higher benefit of the worth knowledge provides. Making higher use of your train tools, nevertheless, continues to be up to you.

To discover out extra about knowledge infrastructure that may aid you cut back knowledge waste, learn this technical paper.


About Ellen Friedman

Ellen Friedman is a principal technologist at HPE centered on large-scale knowledge analytics and machine studying. Ellen labored at MapR Technologies for seven years prior to her present function at HPE, the place she was a committer for the Apache Drill and Apache Mahout open supply initiatives. She is a co-author of a number of books revealed by O’Reilly Media, together with AI & Analytics in Production, Machine Learning Logistics, and the Practical Machine Learning collection.


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