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TDWI Upside - Where Data Means Business

Top 4 Developments for CDOs to Leverage

Chief data officers should pay close attention to these four trends as they increase their use of data and analytics.

Data is the lifeblood of the 21st century's global economy, fueling products and services that show potential to revolutionize entire industries and change how the world works, plays, and lives. A lot of what I do at Deloitte involves working with chief data officers (CDOs) across the globe to help them better manage and enhance their data.

For Further Reading:

3 Steps for Establishing Your Chief Data Office the Right Way

Before Hiring a Chief Analytics Officer, Answer These 3 Questions

Q&A: What's Ahead for the Data Landscape

Here are the four things I tell them to pay most attention to as they enhance the way they use data and analytics to conduct business.

#1: Artificial intelligence and cognitive technologies are at the forefront of digital transformation, so consider getting on board sooner rather than later

After decades of development, artificial intelligence (AI) and cognitive technologies have matured. They are now more accessible, affordable, and valuable than ever – they're essential tools for conducting and expanding business. Cognitive technologies -- by which I mean technologies that can perform and/or augment tasks, help better inform decisions, and create interactions that have traditionally required human intelligence, such as planning, reasoning from partial or uncertain information, and learning -- can enhance product and service development as well as improve operations. The vast potential in the application of cognitive technologies will likely spur continued investment and interest.

Indeed, investment in cognitive technologies is occurring worldwide. The 2017 Deloitte State of Cognitive Survey, which surveyed 250 "cognitive-aware" leaders within "cognitive-active" companies, found that:

  • 87 percent of respondents consider cognitive technologies to be important or very important for their offerings
  • 92 percent believe these technologies are important or very important for internal business processes
  • 76 percent think cognitive technologies will "substantially transform" their business within three years

The advantages of cognitive and AI technologies are many, with the potential to augment, enhance, and even replace human effort. This could come in the form of robotic process automation (RPA), assuming repetitive tasks (e.g., processing customer address changes), improving data accuracy, and releasing human resources for other duties.

Increasingly, AI is also capable of automating tasks that previously required human intelligence, such as facial recognition or the operation of motor vehicles. Looking ahead, language processing and contextual insights may no longer be dispensable capabilities. Instead, they are now business requirements for competing in the data-driven marketplace.

For CDOs, AI and cognitive technologies should be at the forefront of data management strategies. Utilizing the power of cognitive computing capabilities can allow you to achieve a new depth of data insight and analysis and allow for true organizational transformation.

#2: Data management is more important than ever

Organizations must be able to respond to evolving customer needs, sentiments, and cross-sell opportunities with speed and agility. Enterprises that can access the data they need – and can use the data effectively – are most likely to achieve the expected results

For Further Reading:

3 Steps for Establishing Your Chief Data Office the Right Way

Before Hiring a Chief Analytics Officer, Answer These 3 Questions

Q&A: What's Ahead for the Data Landscape

Master data management (MDM) solutions, in particular, are becoming key to helping businesses across all industries maximize the value of their data. MDM solutions can consolidate all types of data and create a single, authoritative view of a business, integrating critical data from disparate, duplicate, and perhaps conflicting systems and sources.

Take the example of a large, national healthcare provider that was able to link all physician notes, lab tests and results, and other data with a common identifier, providing everyone participating in a patient's care with a single, accurate view of the data. Their MDM solution also helped provide a clear view of longitudinal provider interactions that could follow patients across services and sites for the complete cycle of care, including hospital visits, outpatient appointments, testing, and other interactions. Ultimately, this created better experiences for patients, allowed care providers to treat patients more effectively, and improved claims processing with respect to time, follow-ups, and efficiency.

When discussing data management with CDOs, I recommend that they always start with the business value in mind. MDM efforts that aren't squarely tied to business initiatives (e.g., risk aversion, new revenue opportunities, etc.) or to use cases tend to fail because it's hard to keep everyone focused on efforts that appear to lack stakes or the ability to prove ROI. Connecting all MDM initiatives to your organizational goals and strategies will help you achieve the expected results in the short and long term.

#3: Expect more work to move to the cloud

The cloud isn't just for data storage any more. Indeed, the demand for cloud computing solutions is fueling innovation. For its part, Atlanta-based cloud provider ATADATA (whose assets Deloitte recently acquired) offers a cloud platform with automated, integrated cloud migration, discovery, application mapping, mirroring, and migration capabilities for enterprise workloads at scale. This is just one example of how new technical capabilities and innovation are attracting enterprises to the cloud. Expect this trend to continue, and, in fact, be augmented by the rise of hybrid clouds.

One important advantage of a hybrid cloud strategy is that it provides a way for businesses to segregate confidential data that may be too sensitive for available public cloud solutions. In these cases, a hybrid cloud solution allows an enterprise to separate workloads according to the security level they require, preserving the most sensitive information within a private (often on-site) cloud while turning to a public cloud for other data analytics services.

As most CDOs will argue, data is the most important part of an organization's success, so making sure that data can both perform well – meaning it can be used to derive insights that inform business decisions – and be secure is essential. Cloud-based solutions can help do both, appropriately securing and storing your data to help ensure you can use it how and when you (and only you) want.

#4: Data privacy should be at the forefront of your data strategy

Although advances in data analytics are yielding world-changing insights and capabilities, they're also creating privacy concerns that can imperil sensitive consumer and business information.

As I mentioned, making sure your data is secure and can't be accessed by a third party is paramount to your organization's success (or you potentially risk putting the reputation and long-term viability of your business on the line). For CDOs, the key to an effective data privacy strategy is to focus first on risk mitigation rather than compliance requirements.

When you design data security policies and procedures for current regulations, you're not future-proofing your solution. Data privacy is not a one-time effort; it's something that should be continuously monitored and enhanced. Work towards making sure it stands the test of time by designing privacy measures that guard against security breaches and diminish risky data procedures you may have.

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