Why all the fuss?
You would think that data anayltics is an obstreperous power that threatens to disrupt the status quo of business, economics, industry, and life as we know it — if you believed all the hype that surrounds this subject of late. It does sound a bit over-the-top, doesn’t it? After all, data science and analytics are not modern inventions. Data analysis is rooted in statistics, which has a rather long history. It is said that the beginning of statistics was marked in ancient Egypt, when Egypt was taking a periodic census for building pyramids. You could even argue that data analytics is as old as mathematics itself — and that’s pretty old.
Is the reality of data analytics, then, far outstripped by the hype that surrounds it? The subdued response of many business leaders would lead you to conclude that this is indeed the case, as many enterprises continue to make decisions and form strategies based upon hearsay, instinct, guesswork – and the ubiquitous spreadsheet.
However, some rather smart and informed people are jumping on the anayltics bandwagon and proclaiming the pervasive gospel of informed insights:
The role and importance of data analytics cannot be overstated. It is crucial to any business aspiring to seize and capitalise fully upon the information advantage. In its newly published worldwide business analytics services forecast, the International Data Corporation (IDC) forecasts that business analytics services spending will reach $89.6 billion in 2018 up from the $51.6 billion spent in 2014, representing a 14.7% compound annual growth rate. (Seizing the Information Advantage – PWC)
Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage. (The age of analytics: Competing in a data-driven world – McKinsey & Company)
From the invention of digital computers, to the development of relational databases, data warehousing, and data mining, modern progress in the field of data analysis has been steadily ramping up. The invention and adoption of the internet was a game changer, which, combined with exponential advances in digital storage and performance, has completely transformed the data landscape in the 21st century.
What does this all mean?
The world’s most valuable resource is no longer oil, but data.
…as big data sets become staggeringly large, they change the nature of business decisions. Historically, computation was performed on data samples, statistical methods were employed to draw inferences from those samples, and the inferences were in turn used to inform business decisions. Big data means we perform calculations on all the data; there is no sampling error. This enables AI—a previously unattainable class of computation that uses machine and deep learning to develop self-learning algorithms—to perform precise predictive and prescriptive analytics.
The benefits are breathtaking. All value chains will be disrupted: defense, education, financial services, government services, healthcare, manufacturing, oil and gas, retail, telecommunications, and more. (Why digital transformation is now on the CEO’s shoulders – Thomas M. Siebel)
Are we overstating the fact? Not at all. Data has an unprecedented potential to radically transform the very way we live and work. And it is doing so as you read this article.
There are five recent developments that cause me to believe that the data hype does not even approach the reality:
1. Big Data
This phrase has become rather cliche, but the reality is that the world’s data lake is getting big — really big.
Ninety percent of the data in the world today has been created in the last two years alone. Our current output of data is roughly 2.5 quintillion bytes a day. As the world steadily becomes more connected with an ever-increasing number of electronic devices, that’s only set to grow over the coming years.
Early adopters of Big Data analytics have gained a significant lead over the rest of the corporate world. Examining more than 400 large companies, a recent Bain & Company study found that those with the most advanced analytics capabilities are outperforming competitors by wide margins:
The leaders are:
- Twice as likely to be in the top quartile of financial performance within their industries
- Five times as likely to make decisions much faster than market peers
- Three times as likely to execute decisions as intended
- Twice as likely to use data very frequently when making decisions
Smart decisions — good decisions — are data-driven decisions. If you make strategic choices based upon human intuition, dodgy data, or secondhand information, you’re headed for trouble.
By the way, remember that 90% of the data in the world was created in the last two years alone? Yet, less than five percent has been analyzed.
2. Cloud Computing
In the early 2010’s, Amazon Redshift, which is a data warehouse on cloud, and Google BigQuery, which processes a query in thousands of google servers, were released. Both came with a remarkable fall in cost, and lowered the hurdle to process big data. Nowadays, every company is able to get an infrastructure for big data analysis within a reasonable budget. Even startups, which traditionally did not have a budget to conduct such analysis, are now able to repeat PDCA cycles rapidly by using big data tools such as Amazon Redshift.
What is cloud computing? It is the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.
Cloud computing enables ubiquitous access to shared pools of configurable system resources and high-level services that can be rapidly provisioned with minimal management effort. Cloud computing relies on sharing of resources to achieve coherence and economy of scale. Third-party clouds thus enable organizations to focus on their core business instead of expending resources on computer infrastructure and maintenance.
How has cloud computing changed the data landscape? It has made powerful systems and software available to even small businesses and startups, at a fraction of the cost of big, expensive on-site systems.
3. Visualization Techniques
We are a visual society. We might operate from clunky charts and complex spreadsheets when we are forced to, but people excel when they are empowered by elegant, simple, and functional data visuals.
Data is beautiful — just ask David McCandless:
The ability to gather, process, interpret and visualize data is essential for the growth and power of data analytics. Today we see an unparalleled opportunity to create such intelligent and effective analytics through the availability of affordable and intuitive data analytics visualization systems. Powerful analytics can even be displayed and dissected on mobile devices:
4. Predictive Analytics
What business leader or entrepreneur wouldn’t like a magic crystal ball whereby he or she can look into the future? Machine learning and artificial intelligence, empowered by big data, are creating such a digital crystal ball. Not only can we see what happened (descriptive), why it happened (diagnostic), and what we must do (prescriptive), but we can now see what could happen (predictive). This is the power of predictive analytics.
Has your company, for example, developed a customer lifetime value (CLTV) measure? That’s using predictive analytics to determine how much a customer will buy from the company over his or her lifetime. Do you have a “next best offer” or product recommendation capability? That’s an analytical prediction of the product or service that your customer is most likely to buy next. Have you made a forecast of next quarter’s sales? Used digital marketing models to determine what ad to place on what publisher’s site? All of these are forms of predictive analytics.
Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future. The patterns found in historical and transactional data can be used to identify potential risks and opportunities.
Cloud computing is making these predictive tools available to the masses like never before, yet few are the visionaries taking advantage of them.
5. Analytics Platforms
A recent development that has impacted the business world is the rise of intuitive analytics platforms that empower business analysts, not traditional data scientists, to gather, process, transform and interpret Big Data:
Non-Data Scientists will perform a greater volume of fairly sophisticated analytics than data scientists…the reality is that advanced analytic platforms, blending platforms, and data viz platforms have simply become easier to use, specifically in response to the demands of this group of users. And why have platform developers paid so much attention? Because Gartner says this group will grow 5X as fast as the trained data scientist group, so that’s where the money is.
There will always be a knowledge and experience gap between the two groups, but if you’re managing the advanced analytics group for your company you know about the drive toward ‘data democratization’ which is a synonym for ‘self-service’. There will always be some risk here to be managed but a motivated LOB manager or experienced data analyst who has come up the learning curve can do some pretty sophisticated things on these new platforms.
A number of organizations, from Google to Salesforce, have built and deployed powerful, advanced analytics platforms that employ artificial intelligence. These have the potential to enable “data laymen” to perform sophisticated data analytics upon big data sets. Unfortunately, the demand for professionals with the business acumen and technical skills far outweighs the supply.
It is abundantly clear to the informed observer that the reality of data analytics exceeds the hype. Big Data and the tools that utilize it, such as machine learning and artificial intelligence, are partnering with advanced data analytics to usher in the fourth industrial revolution.
There are many areas where data analytics can be relevant, including: improving existing products and services, improving internal processes, building new product or service offerings, and transforming business models. Tragically, when it comes to data analytics, most companies are opportunity-rich but strategy-poor.
The question is not, “Is data analytics important for our business?” Rather, the question is,
“What are you doing about it?
- A brief history of data analysis
- 6 predictions about Data Science, Machine Learning, and AI for 2018
- Seizing the Information Advantage
- The age of analytics: Competing in a data-driven world
- The world’s most valuable resource is no longer oil, but data
- Big Data: Are you ready for blast-off?
- Big Data: The Organizational Challenge
- Data Never Sleeps 5.0
- How much data does the world generate every minute?
- Information is beautiful
- What is predictive analytics?
- A predictive analytics primer
- Why digital transformation is now on the CEO’s shoulders