The currency of tomorrow isn’t what you think: It’s not cold hard cash, precious metals, land, or even cryptocurrency—it’s data. In the very near future, every company in the world will either buy or sell data as this corporate asset continues to gain value.
But, it’s not enough to have access to vast amounts of data, you need to understand it and use it.
The promise of AI is that knowledge gained from applying analytics to the wealth of data that is available today will enhance any decision-making process with additional intelligence, helping us produce quicker, more effective outcomes."
I was talking to a colleague recently about the growing importance of data to business and expressed the view that companies who aren’t collecting and using data might have an impossible hurdle to leap in the race for innovation and competitiveness. This might seem like a harsh view, but without access to hyper-relevant data for decision making, companies will quickly become isolated and disconnected.
AI systems can access and analyze large datasets, so if businesses are to take advantage of the explosion of data as the fuel powering digital transformation, then they’re going to need artificial intelligence and machine learning to help transform data effectively, so they can deliver experiences people have never seen before or imagined.
The Data Dilemma
Nearly every customer I meet these days is focused on two main questions:
- How do we harness the power of data to make better decisions and data-driven decisions?
- What do we need to do to build data science and artificial intelligence capabilities in our organization?
Recent research underscores the importance of data to a company’s bottom line so its not surprising companies are focused on making their data relevant. Consider this:
- By making 10% more data accessible, a typical Fortune 1000 company will see a $65 million increase in net income.
- A Big Data Executive Survey by New Vantage Partners found 85% of businesses want to be data driven, but only 37% have been successful.
- Only a minuscule portion of available data is currently being analyzed so AI could really help here.
The Fuel that Runs Business
If data is the currency of tomorrow, why aren’t we more successful in harnessing it?
A few short years ago, business treated their data as the exhaust portion pumping out the end of many business processes or transactions. Its value was viewed as somewhat limited. Today, data is not an output or a by-product but rather the fuel running our business.
AI could double the annual economic growth rate by 2035, and boost labor productivity by up to 40%."
Data is driving the customer experience, and analytics, machine learning, and AI running on advanced hardware platforms are empowering companies to look at data as a strategic enabler rather than an output product. A few examples include the hyper-personalization of a retail experience, location sensors that help companies route shipments for greater efficiencies, more accurate and effective fraud detection, and even wearable technologies that provide detailed information about how workers are moving, lifting, or their location to reduce injuries and increase safety. And that’s just the beginning. Advanced algorithms are analyzing genetic information to help to detect disease earlier than ever to save lives.
Making Sense of All the Data
If we can all see the incredible benefits and potential, the question remains: Why aren’t companies harnessing this raw power? I believe it’s because many companies are struggling to overcome a few major hurdles including:
Resources: The expertise to make sense of the data is woefully lacking in America today as we cope with a worldwide shortage of data scientists. At the same time, a lack of diversity among data science professionals can further skew results due to unintentional gender or cultural biases that can enter the analysis.
Data Aggregation: We have data coming in from a wide range of sources, including mobile, web, retail, IoT, and more. Aggregating data from disparate sources is complex, but gives a more complete picture of trends and will help improve decision-making.
Data Lies: What I mean by that is that every data set has missing, erroneous, and deceitful data inside it. We have to train data scientists to identify and filter out deceitful and erroneous data, and we need to use better approaches to fill in gaps and improve the accuracy of results.
Cleaning Data: As I , a lot of the time spent by data scientist today is cleaning data to make it accessible, but this is a role that could be turned over to AI-powered machines. This data cleaning process is critical, because analyzing raw data will leave you with insights that are, quite simply, wrong.
Truth Seekers: The most significant thing we need to do is to stop talking about data science as if it leads to truth. Data science does not lead to the truth. Data science leads to the probability that something is correct. It’s a subtle but importance nuance.
Data Literacy is a Core Business Skill
How close are we getting to harnessing the data fuel stores in our organization?
91% percent of organizations have yet to reach a 'transformational' level of maturity in data and analytics."
Transforming data to business value is harder than many companies thought it would be, requiring deeper resources, more expertise, and harder work than expected, but unless you’re planning to buy a one-way ticket to a deserted island, investing the time is essential to future survival.
I believe that reaching the transformational level will require data literacy skills permeating an organization from top to bottom, as well as AI and machine learning to make sense of our growing datasets. And, just like we need to upgrade skills for the mechanics of today to manage the highly digital cars of today, corporations need to begin building data capabilities to ignite their data fuel and accelerate transformation.