Monday, July 01, 2019

Even if big data analytics only entered the business lexicon recently, the concept has been around since forever. Of course, back then, people were manually crunching and analyzing numbers on log books and spreadsheets.

However, big data analytics has never been as well entrenched as now. A company that fails to account for internal and external factors in the course of operations will more than likely fail, especially in the digital age.

And companies know it, too.

For instance, the big data industry is expected to more than double from the $42 billion in 2018 up to $103 billion by the year 2027, according to the report from Forbes. In the same report, almost 8 in 10 executives believe that organisations that do not incorporate big data into their businesses will likely die.

However, with the advances in technology, big data today has become previous generations can dream of in terms of efficiency and speed.

That’s both a blessing and a curse.

Your business really needs to be conscious of constantly updating your technology, knowledge, and processes. By all means, this is not free.

Typically, if companies are not capable, they bring in specialists to do this task for them. There are also software products that not only gather data but analyzes all these pieces of information, as well. After which, the report is laid down for the executives to digest.

Impact of Machine Learning in Big Data Analytics

The amount of data transmitted all across the cybersphere is mind-boggling. No human mind can keep up with all that information. For instance, in just the last two years, the data generated is equivalent to 2.5 quintillion bytes per day.

To give you an idea of how large that is, if you count normally starting from 1, it will take you more than 31 years to reach a trillion. A quintillion is one million trillion.

The only way to aggregate that kind of data is through computers.

Imagine sorting through a heap of trash as high as Mt. Everest to find a single piece of candy wrapper. Not only that, you have to find another piece and analyze the correlation between these two pieces.

Therefore, the challenge is not just to gather the information. The challenge is to collect the right information and analyze how you can use this to improve your processes, boost your status in the marketplace, and sustain your revenue streams.

This is where machine learning comes in.

The beauty about this technology is it’s constantly learning. The more information you feed into the machine, the better and more accurate the results you are going to get. This is why it’s perfect for processing big data.

Of course, this is not an easy process.

Setting up your machine learning system doesn’t mean installing everything into this new environment. Just like most things in life, it’s quality over quantity. This will not only help prevent wastage, but it also helps you save on cost.

There are so many software packages out there and they all have their respective dependencies, as well as co-dependencies. Most of them are not related to your requirements. The most important thing is to install the right software that can deliver results.

With that said, in their 2010 book, Analytics at Work: Smarter Decisions, Better Results, authors Thomas Davenport, Jeanne Harris and Robert Morison argued that companies don’t have to jump on the big data analytics bandwagon right away.

Just because others do it and have been successful doesn’t mean it’s the right solution for you.

Based on their research, companies make the mistake of:

  • Investing in analytics even if it doesn’t apply
  • Spending money on big data analytics even if it’s impractical
  • Relying too much on the results churned out by the machine
  • Not combining reasoning and logic with analytics
  • Forcing analytics when their own processes are not ready to absorb the change

How to Use Big Data Analytics to Help Manage Risk?

There are many reasons why organisations need to overcome their fear and apply this change into their processes.

Here are some of them:

  1. Cut down employee turnover -- Employee attrition is expensive. A study by the Center for American Progress revealed that replacing a typical worker will cost about 21% of the position’s annual salary. In terms of numbers, an hourly wage will cost the company from $3,300 to $4,200 to replace. Big data will analyze your processes and workflow to determine why employees are leaving.

  2. Improving your workflow and processes -- Analytics will not only crunch numbers for new market opportunities, but it will also subject your processes and workflow to stress test to determine strengths and weaknesses. The risks include strategic risks, cyber risks, market risks, external and internal risks. It will help you validate or reject current models, monitor transactions, and employee interactions. The results will help decision-makers mitigate and manage the risks of the organisation.

  3. Detecting fraud -- With the right parameters, big data analytics can be used to detect fraud within your organisation. Think of it as having your very own detective on the payroll, one that doesn’t sleep or rest until the fraudster is identified. This is crucial in healthcare, social security, and insurance services. They can use big data analytics to filter raw information into the system and pick out anomalies. As you plug the hole, your profits increase. Meanwhile, the government is also using this technology to comb through voluminous data to identify and punish tax evaders.

  4. Identify project weaknesses -- Launching a project takes time and resources. For the most part, companies trust their executive and project managers to see the project through. However, this system is mostly output-based. Nobody really questions how it’s done as long as the results are delivered. Without a thorough evaluation, project managers would be doing the same thing over and over again when, in fact, there’s a better alternative all along. Running the project through predictive tools and machine learning will help spot weaknesses in the implementation.

  5. Cut losses during expansions -- This also holds true for new entrants, as well. When your business expands to another area, you need all the information you can get for actionable data. These include the number of competitors in the area, potential client base, land valuation, traffic data, local taxes, labor cost, supply chain, logistics, and others. All these pieces of information will be collected and analyzed to see if setting up a branch in the area makes good business sense.

  6. Managing financial risks -- Big data analytics is important for managing credit risk, commercial and personal loans, along with overhead and logistics. As your business grows so does the potential for risk. New information is generated every day and all the data should go into your digital piggy bank to be pulled out later on. Meanwhile, fraudsters continue to attempt new schemes in order to cheat customers and businesses out of their hard-earned money. You need to be always ahead of them.

How to Implement Big Data to Minimize Risk

The next step is implementing big data analytics in your business. But you have to be conscious of the following:

  • Evaluate your processes first to determine readiness. Forcing change on resistant workers may be counter-productive.
  • Start small. The changes should not be sweeping right away. Start with your marketing team, for instance, or with your IT. This will help you identify the weak spots to be addressed as you scale up.
  • Bring in inputs from your personnel. A worker who feels alienated will quickly distance himself or herself from the organisation. Make sure they know their inputs are important. Once they recognize the potential of big data analytics to essentially simplify their work, they would be less resistant to change.
  • Embed machine learning in the workflow. This will help take out the burden of administrative work from your employees. In that way, they can focus more on the scaling up their contributions to the company other than menial work.
  • Encourage a sandbox approach. Get your project managers and data scientists to test out big data analytics as it relates to your own unique systems. The result of this testing will help your organisation come up with protocols and business models. In the same vein, these new business models should also be stress-tested constantly to determine if they are still relevant to the new challenges.

Conclusion

The concept of analyzing data to help improve business decisions is not new. It’s been there since time immemorial. So why are businesses so resistant to adopting big data analytics? Part of the reason is the lack of knowledge about what it does and how it’s important to their operations. In fact, it’s a matter of life or death in most cases but they fail to recognize this.

In an age where information is not only power but fundamental to your survival, companies have to choice but to invest in big data analytics. You may have the best minds in the business but they are walking around with blindfolds on without actionable information delivered to them at the right time.