How Corporations Can Leverage Big Data to Drive Efficiency
Big Data is everywhere; it’s made its way to every sector of the global economy.
According to International Data Corporation (IDC), the digital universe is expected to grow to 44 zettabytes by the year 2020.
This drives businesses to seek new ways of capitalizing on information.
Companies hire and train data scientists, utilize analytical software and implement modern data mining technologies, like TDA; they’re convinced, quite sensibly, that the insights hidden in Big Data can help them conceive new business models, drive operational excellence and, most importantly, innovate.
[Tweet “Big data analytics to become $16 billion industry by 2025”]
Today, we’ll discuss tips that can help enterprises unlock the power of Big Data completely.
So, how can your company leverage Big Data and Analytics?
1.Never stop seeking for new ways of data exploitation.
Most of the time analysts start with asking specific questions of their data sets. They come up with a hypothesis and then turn to clients records, and observe tendencies within them, to prove (or disprove) the validity of their ideas.
To leverage Big Data, however, they’ll need to think more broadly. Each time a new pattern in a data set is revealed, be it a minor or significant one, data scientists must ask themselves: are there any other, non-obvious applications of this info that we might be missing? They should always brainstorm and dedicate time to research the matter thoroughly.
Here’s how using this approach worked for Amazon.
Amazon, obviously, has lots of clients’ data. Like other e-selling giants, it stores credentials, addresses, phone numbers, search and payments histories of millions of people from all over the globe.
The company uses records foremost to build efficient and personalized advertising algorithms. But, also, unlike many of its rivalries, it utilizes data to enhance customer service.
[Tweet “Big Data helped Amazon reduce cost of support, increase customer satisfaction”]
When a client reaches out to the site’s help desk, a support agent on the other end already knows all the pertinent info about them. Hence, no need for introductions and users aren’t asked to type in their names many times over. They just explain briefly what is it they need, and then promptly receive clear suggestions as to how their issues can be resolved.
Not only does this allow Amazon to reduce costs for support services; it increases greatly customers’ satisfaction and thus improves, even more, the retailer’s robust reputation.
2.Invest in talent.
According to IBM’s report, 90% of the world’s data has been generated over the course of 2015 and 2016. The paper states that our digital universe has been expanding by 2.5 quintillion bytes a day.
That is an astonishing rate, but it is likely to still grow in the future.
Today we have a numerous new technologies – new sensors, data capturing gadgets, etc. – emerging on the market, and corporations, who are exposed to a plethora of clients’ records daily, may find themselves lacking analytical capabilities to mine the data properly.
The talent pool for data scientists is now scarce, while the demand for them is soaring. Therefore, we suggest you put no small effort into building an efficient data team of your own; you should invest generously in recruiting and pick candidates carefully.
These are 4 Big Data-related roles we recommend that you consider:
- A data architect to design data systems and set up robust data-related processes
- A data engineer to scale and enhance data solutions
- A data scientist to derive insights from Big Data using sophisticated math and statistics-related methodologies
- A data translator to find ways of turning analytical insights into actual profits for your company.
The deficit of elite data analysts is what makes some companies acquire AI startups. In 2014, for example, Google spent over 500 million dollars to acquire DeepMind Technologies. At that point, there were roughly 75 data experts working at the startup and, therefore, they cost Google about $7 million each.
Soon after the acquisition, however, the company launched AlphaGo – the first AI-powered program to beat a professional human player in Go. And, according to the DeepMind’s report, the solution enabled Google to reduce its data center cooling bill by 40%, thereby saving the tech-giant a few hundred million in yearly expenses.
Impressive, don’t you think?
Many Global 500 corporations turn to advanced analytics companies, such as Ayasdi, to tackle data issues. They purchase tools to automate data-processing, adopt latest machine learning, statistical and geometric algorithms to at once increase the efficiency of data analysis.
3.Build a corporate culture that is conducive to entrepreneurialism.
You never know from whom an innovation might spring from, so you want each of your employees to feel comfortable voicing their initiatives. Also, you want them to be able to understand data, at least to a degree, so that their ideas are grounded in stats.
Here are a few directions in which your company should develop to make the shift happen:
- Encouraging openness and collaboration between silos in your corporation
- Providing incentives for those who come up with new initiatives
- Creating a separate innovation funding pool
- Establishing specialized metrics to measure the value new initiatives generate
- Making innovation a part of your staff’s development process and reviewing annually if employees make progress
To get inspired, look at Munich RE – one of the world’s top reinsurers.
The company that relies heavily on Big Data and sustains a robust innovation ecosystem has had a unique internal tool developed that allows each member of their staff to submit ideas.
They also have Idea bootcamps, where the initiatives are elaborated on and turned into holistic concepts, and the Innovation Lab, where the approved ideas are finally prototyped.
Big Data grows rapidly and even the largest enterprises, with billions of dollars in revenues, are being awed by its staggering volume.
But world’s digitalisation still advances; businesses accumulate more records and, thus, become faced with a tremendous pressure of having to analyze the data efficiently. They must innovate, according to insights they extract from data, and invent business models that are superior to those of competitors. Otherwise, they risk being pushed out of the market.
As a company runner, who strives to unlock the full power of Big Data, you should repeatedly ask yourself this:
Are there analytics integrated into my firm’s innovation processes?
Do we collect and analyze client’s records, such as social media comments, emails and so on? Are there ways to exploit this data more?
Is there a measurement system in place to gauge whether our innovation efforts are moving the needle?
How familiar are my employees and executives with Big Data? What can I do to improve their analytical skills?
That’s it. We hope the aforementioned tips help you capture the meaning of large data sets and instill a data-driven, analytical, learning-oriented culture within your corporation.
Should you have more questions about Big Data, feel free to contact our expert – a cheerful and very knowledgeable man.