Blog Denave

11th Dec, 2018

Decrypting the Dark Matter of Sales

Dark matter is often a term which is understood very well in the realm of physics, however, what if we say that the terminology holds relevance even in the world of sales? Yes, the infamous yet very much existent – dark data! The same way scientists have been struggling with the dark matter since ages, the dark data also haunts marketers and organizations alike.

While the stakes may be different but the curiosity around the exploration of those dark dungeons and bringing them to light has allured many a number of database managers, analysts and data scientists since long. Data, after all, has become such a competitive currency in today’s world that it is hard, and rather risky to ignore even a dime of data.

What is Dark Data and Why Should It Matter To You?
Simply put, dark data is the data which is being collected or have been collected by the organization but is just lying stored somewhere and is not being processed or analyzed and hence, not contributing an ounce to the competitive intelligence or business to business sales decision making process. In the context of sales enablement, the information which you possess but still not use or leverage for enhancing your sales can simply be called as dark data for your organization.

In terms of its features, dark data is often unstructured, untagged and at times, even qualitative in nature (for example, social media posts or audio or video files). Even structured data content such as contracts supplier reports etc. can also become dark data in a matter of time. Offhandedly (and with a tint of humor), it can also be called as the neglected neighbor of big data because even if its worth remains a mystery, in terms of size, it stands at a gigantic stature.

But please note, while you may not be using those dumps of data – termed as dark data, it doesn’t mean that it can be labeled as useless. Multiple times, owing to the difficulty, both in terms of efforts as well as the associated costs of converting it into a structured format and analyzing it afterward, businesses often prefer not touching that data at all.

At times, it would have been an ‘in-the-moment’ correct decision owing to the cost-viability associated with the structuring and insight extraction but most of the times, in the longer run, the business ends up missing on something which could have acted as an edge in the market.

In the latter scenario, the timely analysis would have also revealed if the cost of accumulating that data could (and should) have been amplified or stripped down. More often than not, valuable business, customer and operational insights are resting in the unexplored recesses of those deep data repositories.

How Big is the Dark Data Universe?
As per IDC claims, almost 90% of the data collected by organisations globally ends up being the dark data. As per another comprehensive industry study, global organizations hold on average 52% dark unclassified data, 33% redundant, obsolete & trivial data and only 15% of identifiable business-critical data.

As per the same study, if the trend continues, this could equate to $3.3 trillion of avoidable storage and management costs by 2020. As per IDG, dark data is growing at a rate of 62% per year and by 2022, 93% of all data will be unstructured.

There are instances when the organizations are not even aware of how much dark data is lurking in their storage. Companies, many a time, may knowingly choose to ignore the exploration of this aspect given that storage space costs aren’t stabbing them that bad at that moment. However, the point which gets missed is that storing as well as securing (which is required most of the times) can be even more risky as well as expensive than what would have been perceived.

Dark Data Proofing of Your Sales Strategy
Just acknowledging the existence of dark data and its status as a menace in your overall business strategy and budget is like touching the tip of the databerg, if we can say! But nonetheless, the first move ought to be made one day or the other.

With the newfound realization around the dangers of dark data – in terms of cost, risks, and missed opportunities, organizations are taking up the task of managing it quite seriously now. A much in-trend word often heard in the dark streets of data is AI-driven dark analytics. However, even when the tool is known, still, the dexterity and complete knowledge of right leverage of the tool is must if the risk of sinking deeper in dark data grave is to be avoided.

The idea of a sound and dark-data proof sales strategy is to first deal with the dark data at hand – either make a rational use of it or otherwise discard it, and secondly, to ensure the dark data doesn’t get accumulated in the system again.

Let’s see on a macro level what should be on the to-do list of all c-suite executives when it comes to defining a dark-data fighting strategy for their organizations:

Activating the Discerning Radars
This is the most critical step wherein the decision makers need to ensure that they have complete visibility into what all data is being collected and what all is eventually needed – thus, doing the first round of sieving and requisite trimming then and there itself.

In case of looking at the existing dark data, one needs to remember that not all the data is useful and a strong differentiation between what matters and what doesn’t, is mandatory. After all, storage space which in general scenarios is terabytes and more of useful space should be thought of as a factor of investment/ money going from the company kitty.

Acting on the First Level Insights
Based on the first step discernment, it would be a sheer waste of time, effort and money if the insights are not acted upon. Hence, the data which showcases no signs of being useful, i.e. it is either redundant, obsolete of trivial – get rid of it as soon as possible.

Do keep a lookout for the duplicate files as well and the personal data from organization employees which at times also gets stored in the system and is then forgotten.

Apart from deletion, this part also needs to comprise actions relating to sifting through the data further. Otherwise, running analytics engine on the complete set of dark data would mean incurring costs on the behalf of even non-useful data. Hence, noise and clutter removal for extracting only the good data from the whole bunch of data is the required action.

Taking Complete Control of Present and the Future
While figuring out and sorting the existing dark data is one major sigh of relief for any organization, it is also extremely important to have a mechanism in place which can stop any further accumulation of dark data.

Therefore, strategizing and implementing a data governance policy to put a stop to the culture of data hoarding is without a doubt the most significant step in this whole journey. The IT teams would also need to be involved to bolster-up the data fencing measures and avoid even the accidental proliferation.

Apart from these macro-level insights, there are several other minor steps also which all sales organizations need to put in place to avoid the mess of dark data, such as banning the use of unsanctioned syncs and share devices by employees since a lot many times, the personal data files and duplicate copy of existing data is a resultant of such kinds of access.

Then ensuring the strict adherence of data policies while inculcating a culture of responsible usage of ‘free’ storage spaces, if there are any, also formulates some ground-level steps to ditch the dark data and keep it at bay.

While stitching a sales strategy after ensuring all these steps might be a bit time-taking in the beginning but the same would be rewarding in the longer run.

Having a partner onboard who understands the criticality of data and who knows the art of exploring uncharted data territories in a responsible manner, would ease the burden to a major extent.

Do you think your organization is tackling the issue of dark data aptly? Do you foresee an industry-wide increase in investment when it comes to advanced level analytics leverage for dark-data analysis? Share with us your valuable inputs in the comments section below.

 

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