The explosion of disparate data types and formats may seem overwhelming to even the most fervent of digital junkies, but the information deluge is generating new opportunities for businesses across the board. With data spawned by both humans and machines (sensors, drones, etc.), and from sources such as real-time weather and location-based systems and feeds from social media (think tweets), business strategists are embracing the art of the possible, particularly when it comes to applying advanced artificial intelligence (AI) algorithms. But none of this matters if you don’t have a solid and modern data foundation.

More sophisticated, low-cost technology is emerging that allows for the immediate processing of these once disparate and/or non-existent data sources. This is enabling progressive organizations to shift production on the fly, generate new business models with incredible foresight, and provide services that help with customer retention.   

It All Starts with a Modern Data Foundation

A leading printer manufacturer recently described how a modern data foundation was key to helping it transform its business model. The company needed a data foundation that could gather real-time data from its printers in the field that then fed into a digital twin (replica) of the printer. The goal: predict potential printer failures with higher accuracy and zero-in on the part in need of replacement, thereby averting failure. The manufacturer now delivers a better customer experience through fewer failures (we all know the pain when the printer breaks down!), as well as a significant increase in the effectiveness and cost of service calls. 

In another instance, a leading tech consumer products company found that its continued investment in a legacy data warehouse was undermining its ability to expand the services it delivered to its partners. We’ve partnered with the company to redefine its data foundation by helping it leverage modern computing techniques and tools. The use of new data technology such as ontologies, causality engines and AI-driven meta-data will create an autonomous data pipeline that speeds up the ability to source, curate and transform a variety of data in near real-time, significantly accelerating time to insight.   

Finally, a leading casino operator wanted a better way to understand its customers. This should come as no surprise following Caesar’s acknowledgment that its loyalty program, which is data-driven, was its most important asset (valued at $1B) during its 2015 management bankruptcy filing. The company sought our help in finding data, within the bounds of proper privacy practices, on all customer activities to personalize the customer experience.

The plan is to leverage data modernization techniques that will help the casino operator obtain unified customer data by implementing knowledge graphs and semantic models from all customer interactions within and outside its property in the most effective manner. By doing so, the company can model customer behavior and create customized experiences. Together, we’re building a modern cloud-based data architecture that will enable the business to ingest and curate these transactions to deliver a differentiated customer experience.  

Modernizing Data to Become AI-Ready

Many of the CIOs, CTOs and CDOs I meet are scrambling to figure out how and where they should leverage AI.  My advice is to start with a simple question: “How ready are you for AI?” The answer is multi-dimensional – it’s about your talent vacancy rates, change management and opportunities across the value chain. But from the get-go, it’s also about having a data foundation that can help you turn raw data into a form that AI can reveal as knowledge. 

To get started, you need to consider the following:

  • What is the quality of your data? Quantity does not equate to quality. According to some estimates, only 3% of data meets basic quality standards. It’s important to assess which points in your operating model are most likely to benefit from data and then make sure the data you have is in the right shape and form to provide these benefits. Additionally, you’ll want to establish a data hygiene process that can profile incoming data based on the principles of continuous data quality management. This means identifying data quality issues while defining, updating and maintaining data quality rules to ensure that only cleansed data is sourced by downstream applications.  
  • How quickly can you access data and provide it for real-time insights? The ability to ingest and process data from social media, weather and more to support rapid decision-making is hyper-critical. To remain competitive, businesses need to consider incorporating a data lake to intake structured and unstructured data and develop an analytics environment that allows information to be quickly curated and turned into insights.
  • Do you have the data that can drive AI possibilities? AI requires fast, easy access to data. With a master list of business processes, your team can identify the decisions that need to be made for each process.
  • Does your data foundation have the built-in intelligence to drive outcomes?  Data IQ frameworks such as our Data Modernization Method provide a quantitative measure of whether the information your organization has on hand will assist team members in the decisions that need to be made, such as determining a customer’s long-term potential. Frameworks turn up information that otherwise wouldn’t be apparent. Use this to measure the intelligence of your organization’s data landscape and its readiness to deliver analytics to meet key business objectives.

Collectively, these actions will help you identify your organization’s data intelligence strengths and weaknesses and ensure that your AI initiatives will deliver intended business outcomes.

To get started on your AI quest, please join our “Bridging the Data Disconnect” webinar on Sept. 19, 2019, where we’ll discuss how to use data to fuel your organization’s AI fitness.

Arun Varadarajan

Arun Varadarajan

Arun Varadarajan is Vice President and Global Head of Data within Cognizant Digital Businesses’s AI & Analytics Practice.  With over 25 years... Read more