To illustrate this point, let us take the perspective of an online cloth merchant who is struggling to move units of a synthetic red dress. They have noticed that they are unable to aggregate stocks by design before they destroy, and that their inventory of red synthetics is always left over at the end of the month. In order to help alleviate this woe, our protagonist may first review the ERP system to check on their unsold stock. What they get in turn, however, does not solve their mystery, as only a SKU and inventory count does not answer where exactly the problem resides.
To take it a step further, our online cloth merchant may try and understand if it is a particular shade that is failing to move units – however, between Magenta, Pink, Vermillion, Persian Red, Turkey Red, Japanese Carmine, Raspberry Red, Burgundy, and Mahogany, their heads begin to spin! When coupled with accounting for different types of fabrics, the myriad of designs they offer, and the sizes they have, our merchant begins to realize the issue of understanding exactly what aspect of their red synthetic dress is failing to meet consumer demands is far deeper than they imagined.
At this point, the insurmountable task of understanding exactly where their red synthetic dress fails has revealed itself and they are not even close to scratching the surface of all possible criteria. Seasonal changes, store sales, customer age, time of day, holiday sales, and regional factors all have yet to be taken into account. Micromanaging all these datasets are beyond the scope of reasonable human analysis. While our online cloth merchant may get a much better “sense” from accounting for all these factors, too much data can paralyze decision making when reviewed by human hand. Data Analysts are key in this position, as human intervention to interpret the available data to provide feedback, planning, design and production is a skill that will still be a necessity for some time – yet in the end, people may find themselves lost while parsing through vast data sets.
Instead, rely on Smart Data to help learn from ‘the noise’ of too much data. Smart Data act as beacons of Machine Learning and can learn from historic data to predict the performance of a particular style. Smart Data can deal with millions of datasets in a way that people cannot, and help associate particular datasets with sales to create correlations of which combination of parameters lead to sales. These predictions have been so uncanny that some have gone on to claim that their phones or laptops are recording their conversations to suggest products, while in reality, these algorithms have become so potent they can predict what we will say before we say it. However, when people try to make these same predictions, it is a far more coarse estimate, as it is too time-consuming, expensive, and difficult to review any potential correlation of the endless datasets available. Smart Data can streamline this endeavor to enable rapid planning, design, production, and allocation of resources.
Returning back to our cloth merchant, Smart Data can help get to the root of the problem of whether the red synthetic dress can be a successful product or not. Between programmed algorithms and machine vision, data can be quickly segmented and analyzed to yield useful insights into where the failings of the dress emerge. Thus, Smart Data helps answer questions such as what factors led to successful sales, what factors inhibit sales, what factors can optimize sales, and whether other products in your assortment influence the sale of the dress. The prediction that Smart Data spits out, however, must be reviewed and executed by an intelligent and skilled human. In that way, Smart Data does not necessarily rob your workers of their employment, but may demand a different skillset. Smart Data only gets more accurate with more information, and as a result, it is up to people to get it up to speed.
As a whole, the key of Smart Data in business is managing a dizzying number of parameters in a way that would leave most employees with a headache. However, building that data store and complementing data predictions still relies deeply upon human input. As a result, Smart Data can augment your business and is a digital transformation that virtually all business owners should be familiar with. To linger in the dark on the potency of data management is deny yourself an unmatched, easy-to-use effectiveness and efficiency in number-crunching and sales predictions.