Eric Schmidt, the ex-CEO of Google left us astonished, at Google’s 2010 Atmosphere summit when he said that in every 2 days we generate as much data as it has been collected since the beginning of times. Notably, he was only referring to the data collected up until 2003.
eDiscovery also shared that while 70% of data is generated by individuals like us, 80% of that data is stored and managed by enterprises.
The same source also predicted, that by 2020, 7 megabytes of original information will be created every second.
One couldn’t help but wonder why there’s so much time, energy, and resources being wasted on collecting, managing, and storing it.
There must be a reason, or all these enterprises wouldn’t be giving data a second thought.
There is indeed a good reason and it’s called data analytics.
There’s a number of ways data is analyzed today and used to make all kinds of decision. From inventory to the introduction of a new product, ever process relies on data analysis.
One such form of analysis that is gaining wide popularity is predictive analysis. Regardless of the niche, prediction via data is utilized by various organizations. Matter of fact, the global market of predictive analysis is expected to reach $10.95 billion by 2022.
In order to get an in-depth knowledge on the subject, it’s crucial that we understand predictive analysis.
According to CIO, “predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision.”
With the correct tools and models, any organization can use their historical data and the current data to forecast the future behaviors of their audience. This prediction can be made hours, months, and years into the future.
While some organizations are still learning about it, the ones that are ahead of the time has already reaped many benefits from the predictive analysis. Let’s see some real-time proof of how predictive analysis saved the day for the following companies:
A company like Shell needs no introduction. One of the market leaders in the energy industry, even Shell faced some complications a few years back.
It was chaotic at best. CIO pointed out that there was no integrated system so the company didn’t know which facility was housing what kind of parts, when was it time to restock, and what kind of shortcomings were being missed in the quality department. Most importantly, the machines were malfunctioning but Shell didn’t know how or when, until the parts completely failed to function.
That is when Shell decided to harvest data and leverage it to make better decisions. What shell did was, develop an analytical model that predicated beforehand when any of the 3000 parts in oil drilling machines will fail. This also allowed Shell to tell beforehand when they’ll need to invest in a machine.
Similarly, Shell also used predictive analytics for their inventory management and saved millions of dollars in trying to make sense of the inventory and shifting the stocks so they would be found in the right place at the right time.
Any supplying and distribution business can take some inspiration from Morton’s Steakhouse that went all out to impress their one customer. What they did is, keep tabs on their mentions on social media. After a tweet came to their notice where the original poster jokingly said that he wanted steak sent to Newark Airport, Morton’s Steakhouse’s data team got to work. They dived into his purchasing history, pulled out an order that he was sure to like in the next hour because he ordered it in the past, and delivered it to him.
Starbucks is perhaps one of the most successful businesses to date. This mammoth of a brand didn’t succeed on luck alone, it’s because they recognized the value of predictive analysis early on and used it to their advantage. If you have always been perplexed about how every single branch of Starbucks is a success despite the facts that another is situated just a few steps away, then here’s your answer.
Starbucks doesn’t just inaugurate its new branch anywhere, before that, Starbucks uses predictive analysis to forecast how well the brand would be received. The results are drawn from the data on location, demographics, traffic, and customer behavior, etc.
McLaren has many accolades to its name, but there’s a reason behind it. The racing car team uses predictive analytics that keeps their athletes safe in this extreme sports and ultimately ensures their win. The team company uses data to forecast beforehand if there’s an issue in the race car. Historical data meets real-time analysis and delivers predictions that can save lives and careers.
Issues in logistics vehicles, bad inventory decisions, and awkward locations of warehouses are something most suppliers and distributors struggle with. Yet they can always take a page out the above-mentioned businesses’ book and employ some of the techniques themselves.
Investment in predictive analysis can never be a wrong decision. It will be an asset for any company but especially for those whose wrong decisions can cost them everything. Since the risks are high for every big and small corporation, predictive analysis is no longer limited in its usefulness.
Of course, it is not a game over kind of solution, but is a game-changing solution for organizations who use it wisely. Predictive analysis is allowing businesses to be more effective and also letting it lower its risks of failures that often result from bad decisions.
Omnisys Solutions also helps companies leverage the data they already have to get tangible dollar benefits. With a focus on business value, Omnisys helps its customers make the most of their technology investments and define the right roadmap for them.