The pace of technology-driven change is accelerating for enterprises all around the world. While the idea of artificial intelligence (AI) has been around for nearly 70 years, it wasn’t until 2017 that we found 72 percent of business leaders believed AI to be a competitive advantage in the future (if not already), according to a recent PwC AI survey. In response, it’s critical for companies to iteratively shift paradigms from legacy approaches to better compete in the age of digital transformation.

Evolving software algorithms, capable of performing tasks typically requiring human intelligence, are fueling a wave of advancements in visual perception, speech recognition, decision-making, language translation, robotics and autonomous vehicle capability. Though AI is the catchphrase for numerous subfields, machine learning and deep learning are garnering the most attention as they teach themselves to learn, reason, plan and ultimately become more intelligent when exposed to bigger, more refined data sets and a standard predictive analytics model.

Surprisingly, many enterprise leaders in the PwC AI survey did not perceive artificial intelligence in business to be as disruptive as IoT, despite its integral role in IoT. Fundamentally, well-managed data (from new and legacy sources) combined with AI is poised to drive product innovation, content creation and new engagement models that will define customer engagement disruptive to industries and profit margins in the future.

Intelligence Requires Data

Traditionally, there have been multiple versions of data warehouses with multiple structures within larger enterprises. Ownership of data sets has been vague, governance is formidable and senior leadership can only sporadically rely upon data for decision-making support.

Being competitive requires enterprises that incorporate newer technologies, approaches and skills to mature into more proactive, predictive and prescriptive states. Talent examples include data scientists and experts in big data analytics who can incorporate artificial intelligence, predictive learning models, machine learning and deep learning techniques. The common denominator to getting artificial intelligence in business correct is accessing and mastering the right data.

As the availability and appetite for data continues to grow, the expectations of better insights soar. Hence, the corporate world is witnessing the emergence of a new role in the C-suite, the chief data officer (CDO). The CDO is often charged not only with enterprise-wide governance but also the utilization of information as an asset. This emerging leader now owns the data strategy and is responsible for harnessing data, managing the risk and ultimately creating new revenue-generating opportunities.

Paramount to this success is establishing a robust data architecture that accommodates IoT, big data and evolving regulatory requirements to support a wide range of future business initiatives. Many leaders look to centralize emerging resources, such as AI, where talent is scarce and competition is intense, to reporting functions to make them available to all business units in a self-service mode that will accelerate progress on growth initiatives across the enterprise.

Data Needs a Strategy

As all aspects of information management are evolving, new and old technologies will have to coexist for the foreseeable future. Well-managed data is no easy task, and in these mixed and emerging environments, the equation could get more complicated. It’s a good idea to re-examine the basics by starting with three simple steps. Kick-starting or refreshing even the most well-planned data strategy can help provide impactful course corrections. Getting started can include the following three steps:

1) Focus on (and establish) data governance. Given the nature of today’s regulatory challenges, most firms start with governance to establish guidelines that safeguard from costly missteps. Additionally, an applicable governance plan brings structure and holds the enterprise accountable to build data as an asset.

2) Evaluate data architecture and technology. By defining enterprise-wide technology standards and developing tools that can be reused, enterprises can reduce costs by promoting better planning and consistency around technology changes. Standard data protocols also reinforce governance by classifying products, customers and other areas uniformly and providing rules for where it is stored and how it travels through systems.

3) Develop or extend analytics capability by leveraging existing capabilities. The mandate must include a fresh look at business objectives and a plan to make better use of analytics to achieve those goals. For example, many use analytics and a predictive analytics model to forecast sales based on product and market data to achieve ROI. Others may decide to expand their use of analytics to analyze customer spending habits and adapt product features to certain customer segments to improve retention.

Data Fuels Artificial Intelligence in Business

As with any process, however, enterprises should tailor these steps around streamlining data infrastructure, so they are prepared for emerging AI technologies. While AI might seem too far out there for some, and data too basic for others, these combined elements are now fundamental to how we engage customers and measure the performance of our products and services.

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