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2 posts from February 2017

10 February 2017

Part II Data Governance banana skins: Enterprise data governance – the final step must be the first

by Kiran Gill Senior Associate

If you read the first part of this series, you’ll undoubtedly have been on the lookout for Data Governance banana skins and with any luck, will have avoided a few slips!

In this blog I’ll talk about the silver bullet that will (apparently) resolve all data-related issues in one go – the Enterprise-wide Data Governance initiative. This subject deserves all the airtime it can get – it’s where most programs fail before they even begin.

Enterprise-wide Data Governance cannot be achieved in one hit

Data-gov

I saw a TV series recently and (as I can’t get data off my mind) it got me thinking about the role people play in a successful Data Governance program. The series is centred on a theme park called Westworld, inhabited by pre-programmed, life-like “hosts”. The things they do and say are designed by the writers of the “narrative”. They react to people and situations as expected and the park runs smoothly. They follow a defined narrative… Don’t panic - there are no spoilers here but I immediately started to draw parallels between this park and a typical data dependent organisation. “How?” I hear you ask…

In a business with no clear or adopted governance, there are groups of people (much like hosts); the data producers, modifiers and consumers. They manage data in the best way they know how. Their responses are shaped by legacy processes, misinformation, workarounds and impulsive reactions. I see this as an improvised narrative. Unlike the hosts in the park, there is no real definition on the way the stakeholders respond to badly managed, missing, incorrect, duplicated or corrupt data, introducing excessive risk to the business. Governance is not successfully established and people do what they think is best.

Turning improvised narrative into a defined one requires a well-planned, robust and continuously improving Data Governance program. Many businesses see this as a one-time effort, where a policy is designed and communicated to the business and letting them understand it themselves. All staff, including data stakeholders are expected to (without guidance, context or training) understand, adopt and follow the policy and the program is expected to be a success. This is where failure sets in. People don’t have time to read a policy. They don’t have time to figure it out for themselves. Most of all, they are in no way compelled to act – what is in it for them?

When designing a Data Governance program, the “so what?” needs to be addressed. The stakeholders need to know why this program is important, and why their behaviours and workflows need to be adjusted. This can only be achieved by proving the benefits and communicating them out to the business. Although laying the foundations and establishing the program at an enterprise level is critical to get the buy in, it is not something that can remain an isolated set of events. This is where many programs fail. The enterprise effort needs to be supported by a tactical effort.

PwC’s ‘Operational Data Governance’ is a methodology that embeds governance in a sustainable and iterative way. Our way of thinking turns Data Governance on its head, making the leap from theory to an operational reality. The methodology focuses on developing strategic objectives, policies, models and roadmaps with a view to applying the theory to resolve the priority data issues, defined workflows, roles and responsibilities, data management tools, processes, and enablement, ensure successful deployment and adoption. It allows the business to take the theory and test it through well-planned pilot implementations in prioritised areas.

I worked on a recent project to design and implement Data Governance across the enterprise.  A program and operating model were carefully designed around the organisation and its structure. The objectives of the program, the strategy and the policy were communicated to the business. However, this was not the final step – it was the first. The fundamental difference here is engaging the business from the outset so they knew how to apply this new process.

Writing a set of rules and telling people to follow them, without involving them in the creation of the rules, is not only unrealistic but also unfair. The focus needs to be on informing, enabling and empowering people through carefully planned and focused deployments, allowing continuous improvement of the Data Governance model. The make or break is the way in which this is introduced into the business. A “launch” isn’t the final step – it is merely the first of many.

In the next blog, I’ll talk about planning a route to success using PwC’s Operational Data Governance framework. Until then, if you have an immediate questions or queries, please do get in touch with our Data & Analytics team.

by Kiran Gill Senior Associate

02 February 2017

Using Personal Data to Build Customer Trust and Competitive Advantage

Every time you use a search engine, land on a website, buy a product or download an app, you generate and share personal data via a sophisticated and sometimes covert combination of tracking tools like cookies, beacons and e-tags.

This expansion in personal data collection is creating enormous visibility into individual lives, preferences and behaviours, a trend that will escalate as technology continues to evolve with breakthroughs such as wearable devices, autonomous automobiles and the Internet of Things.

The proliferation of data also represents a potential treasure trove of opportunity for companies seeking new sources of revenue and competitive advantages. In fact, 64% of respondents to PwC’s 20th Annual Global CEO Survey believe management of data will be a differentiating factor in the future. Consider Microsoft as an example. The software titan is integrating data from three of its sources—LinkedIn, Office 365 and Bing—and using that information in new and powerful ways.

Similarly, forward-thinking companies are enriching core products and services in a bid to integrate services with data to build competitive advantages and add value to customers. Still others plan to sell the customer data they collect.

Businesses that are looking to drive value from personal data range from asset services to credit score companies, utilities, retail and consumer products, and telecom firms. Here are some examples of how they are realising the potential of personal data:

– Mining product reviews on social media and usage data from sensors to help companies sharpen research and shorten development life cycles.
– Using consumer data to identify and prevent fraud in online payments.
– Leveraging personal data across insurers, healthcare providers and other healthcare organisations to lower costs.
– Using personal data to screen potential employees and/or customers.
– Selling anonymised client data to enable bench marking in order to identify industry best practices.

Barriers to success in monetising data

Many organisations are amassing a vast quantity of personal data, but most have not fully considered opportunities to monetise that information. While we’re seeing an increased interest in monetisation of data, several trends suggest that businesses should move carefully in doing so.

First, the world’s toughest privacy law, the EU’s General Data Protection Regulation (GDPR), will go into effect next year. Also looming are new demands that businesses disclose customer data for use in criminal investigations.

One concern that’s not new—but is escalating—is personal data breaches. It’s a risk that many businesses understand: A full 91% of respondents to PwC’s CEO survey said breaches of data privacy and ethics will have a negative impact on stakeholder trust in the next five years.

Finally, experience has shown that it is a significant challenge to set up and manage a reliable end-to-end process and infrastructure to monetise data.

Some companies are reacting to this increasingly regulated, complex and risky environment by putting the brakes on personal data and data-monetisation initiatives. Businesses should use caution, of course, but they should take action to get a head start in updating their data policies.

Getting ahead of the crowd

Personal data has traditionally been defined as any information relating to an identified or identifiable person. Today, personal data has new uses and value. It can enable or be exchanged for valuable services and products. Data is also propelling growth and diversification, and spawning new organisational functions and governance models.

As data moves from a secondary asset to a primary asset, the spectrum of risks is expanding. Consequences can range from minor (annoying your customers) to major (regulatory fines and serious brand damage).

In many ways, the increasingly regulated environment is creating an inflection point as well as a grace period. Every company with skin in the personal-data game should use this time to transform data protection and privacy to a board-level issue.

Businesses should take advantage of the grace period to become a proactive protector of personal data, use blended approaches and multiple techniques to give customers choices in how their data is used, and provide consumers who share data with something of value in return. Companies like Google and Intel are supporting initiatives to inform customers about personal data stored about them and offer incentives for reusing that data. Businesses that do so stand to gain long-term competitive advantage through trusted, mutually valuable relationships between the organisation and its customers.

Put data in your business strategy

By now, it’s clear that data will play a critical role in almost every business strategy. That means organisations will need to evaluate their goals and priorities against this question: Which of our goals can be met with data and what data do we have, or do we have access to? Every organisation will have individual priorities, of course, but a data-driven business strategy will typically play a big role in one of these initiatives: Growing sales and revenue, improving customer experience and trust, and building business differentiation.

To get there, companies are increasingly putting data protection and privacy in the hands of business functions to gain a cross-functional, holistic view of the uses for personal data. Others are embedding data protection and privacy functions into individual business units that roll up to a senior executive. Whatever the approach, a sound data strategy should allow business units to easily innovate within the confines of the organisation’s strategy and regulatory compliance.

How businesses use, manage and protect personal data is becoming a measure of organisational transparency and trust. For many, data privacy has become a critical business requirement: This year, 58% of respondents to PwC’s CEO Survey said they worry that a lack of trust in the business would harm their company’s growth prospects, up from 37% in 2013.

As the opportunities to employ personal data for growth and competitive advantage multiply, it’s essential that businesses prove they can secure and appropriately use personal information. Doing so will demand that organisations rethink and update their data-use governance policies, which will be explored in the next instalment of this series.

This is the first of a three-part series about the evolving uses of personal data and strategies to protect consumer data. Original content has been posted here.

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.