The pace of digital change is creating new opportunities for customers and these opportunities require quick responses. The role of the digital executive/officer is one of the most demanding in business. They need to be strategic, creative, growth-minded and cost conscious of the world they live in.
Many organizations are either beginning, or in the midst of, digital business transformation initiatives. The prediction is that only 30% of these efforts will succeed. To be part of that 30%, business and IT leaders must be ready and willing to innovate rapidly from a business model, business process and technology perspective.
As a result of business model innovation, some business processes must become deliberately unstable, and deliberately unstable processes are designed for change and can dynamically adjust to customers' needs. They’re needed because they are agile, adaptable and "supermanoeuvrable" as customers' needs shift. They are also competitive differentiators, because they support customer interactions that are unpredictable and require ad hoc decision making to enable larger, more stable processes to continue.
It is imperative in 2016 to break away from linear business processes and deploy a spectrum of standardized and variable processes to reap the benefits of digital business. The need for this shift is intensified by the introduction of several new factors and many types of unmeasured KPIs/ Internet-connected 'things' etc. into the business environment. Things like smart machines generate real-time information for other machines. Business processes must be designed for change to enable organizations to exploit this information.
There are many aspects to consider in harnessing big data and advanced analytics, and becoming an insights-driven organization. To help data professionals and IT leaders on their journey, here are a few Guiding Principles to not only drive value from big data and analytics, but to also put insights at the heart of your enterprise. Here are those principles:
Governing Principles
Principle 1:
Embark on the journey to insights, within your business and technology context
The starting point must be your digital business objectives. Design your roadmap to harness new data sources based on how they will help achieve these objectives. Equally importantly, your journey must be dictated by where you start, not only in terms of data maturity but also technology.
Principle 2:
Enable your data landscape for the flood coming from connected people and things
Principle 3:
Master governance, security and privacy of your data assets
Insights from unreliable data are worse than no insights at all. Equally, programs fail and businesses leave themselves exposed if data is not handled securely and with consideration of relevant privacy issues. Maturing and industrializing an organization in its production of value from data, is a key lever to success.
Principle 4:
Develop an enterprise data science culture
Principle 5:
Unleash data- and insights-as-a-service
Principle 6:
Make insight-driven value a crucial business KPI
Measure your measurement. Apply data science to your data science to see where you are adding value and where you are not. If data is becoming one of your most valuable assets, then treat it as such – include it in KPIs and business reviews.
Principle 7:
Empower your people with Insights at the point of action
All functions in an organization are faced daily with a series of decision points and actions, both at the macro and micro level. Whether you are in Supply Chain, Finance, Procurement, Marketing or other parts of the business, empowering your business teams with real-time insights at the point of action makes the crucial difference.
From marketing to medicine, personalized treatment is taking hold. Customers across all industries expect more these days, and they will go elsewhere if they don't get what they want. The most advanced organizations are actively addressing this dynamic by blending traditional customer data with big data, then using analytics to fine-tune their products and services.
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Big Data Is Improving Lives of Americans: A White House Report
The White House has just issued a report looking at four of the top areas where big data has the potential to greatly improve the lives and safety of Americans. But there are just as many pitfalls as promises to be aware of.
Big Data’s Opportunities & Challenges for All Americans
The Obama Administration’s Big Data Working Group has just issued its comprehensive report looking at the opportunities and challenges around big data and four key areas of society:
• Personal credit • Employment • Higher education • Law enforcement.
Entitled Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights, the report notes that “big data and associated technologies have enormous potential for positive impact in the United States.”
But big data also has the potential to create unintended discriminatory consequences if not used correctly. Here we look at the Problem the government is trying to solve; the Opportunity that big data presents; and the Challenge that will need to be overcome in order for a big data solution to work.
The Big Data Challenge:
Expanding access to affordable credit while preserving consumer rights that protect against discrimination in credit eligibility decisions.
The right to be informed about and to dispute the accuracy of the underlying data used to create a credit score is particularly important because credit bureaus have significant data accuracy issues, which are likely to be exacerbated by the use of new, fast-changing data sources.
The Problem: Traditional hiring practices may unnecessarily filter out applicants whose skills match the job opening.
Even as recruiting and hiring managers look to make greater use of algorithmic systems and automation, the inclination remains for individuals to hire someone similar to themselves, an unconscious phenomenon often referred to as “like me” bias, which can impede diversity.44 Algorithmic systems can be designed to help prevent this bias and increase diversity in the hiring process.
The Big Data Opportunity:
Big data can be used to uncover or possibly reduce employment discrimination.
Companies can use data-driven approaches to find potential employees who otherwise might have been overlooked based on traditional educational or workplace-experience requirements. Data-analytics systems allow companies to objectively consider experiences and skill sets that have a proven correlation with success.
The Big Data Challenge:
Promoting fairness, ethics, and mechanisms for mitigating discrimination in employment opportunity.
Data-analytics companies are creating new kinds of “candidate scores” by using diverse and novel sources of information on job candidates. These sources, and the algorithms used to develop them, sometimes use factors that could closely align with race or other protected characteristics, or may be unreliable in predicting success of an individual at a job.
The Problem: Students often face challenges accessing higher education, finding information to help choose the right college, and staying enrolled.
Differences in the price of attendance across institutions affect financial returns, and may lead to differences in the amount that students have to borrow, which may also affect their career decisions and personal lives in meaningful ways. Despite the importance of this decision, there is a surprising lack of clear, easy to use, and accessible information available to guide the students making these choices. The opportunities to use big data in higher education can either produce or prevent discrimination—the same technology that can help identify and serve students who are more likely to be in need of extra help can also be used to deny admissions or other opportunities based on the very same characteristics.
The Big Data Opportunity:
Using big data can increase educational opportunities for the students who most need them.
To address the lack of information about college quality and costs, the Obama Administration has created a new College Scorecard to provide reliable information about college performance.56 The College Scorecard is a large step toward helping students and their families evaluate college choices. Never-before-released national data about post-college outcomes—including the most comparable and reliable data on the earnings of colleges’ alumni and new data on student debt—and student-loan repayment provides students, families, and their advisers with a more accurate picture of college cost and value.
The Big Data Challenge:
Administrators must be careful to address the possibility of discrimination in higher education admissions decisions.
In making admissions decisions, institutions of higher education may use big data techniques to try to predict the likelihood that an applicant will graduate before they ever set foot on campus.59 using these types of data practices, some students could face barriers to admission because they are statistically less likely to graduate. Institutions could also deny students from low-income families, or other students who face unique challenges in graduating, the financial support that they deserve or need to afford college.
The Problem: In a rapidly evolving world, law enforcement officials are looking for smart ways to use new technologies to increase community safety and trust.
Local, state, and federal law enforcement agencies are increasingly drawing on data analytics and algorithmic systems to further their mission of protecting America. Using information gathered from the field and through the use of new technologies, law enforcement officials are analyzing situations in order to determine the appropriate response.
The Big Data Opportunity:
Data and algorithms can potentially help law enforcement become more transparent, effective, and efficient.
New technologies are replacing manual techniques, and many police departments now use sophisticated computer modeling systems to refine their understanding of crime hot spots, linking offense data to patterns in temperature, time of day, proximity to other structures and facilities, and other variables. Some of the newest analytical modeling techniques, often called “predictive policing,” might provide greater precision in predicting locations and times at which criminal activity is likely to occur. An analytical method known as “near-repeat modeling” attempts to predict crimes based on this insight.
The Big Data Challenge:
The law enforcement community can use new technologies to enhance trust and public safety in the community, especially through measures that promote transparency and accountability and mitigate risks of disparities in treatment and outcomes based on individual characteristics.
Those leading efforts to use data analytics to create and implement predictive tools must work hard to ensure that such algorithms are not dependent on factors that disproportionately single out particular communities based on characteristics such as race, religion, income level, education, or other data inputs that may serve as proxies for characteristics with little or no bearing on an individual’s likelihood of association with criminal activity.
Looking to the Future
The use of big data can create great value for the American people, but as these technologies expand in reach throughout society, we must uphold our fundamental values so these systems are neither destructive nor opportunity limiting. Moving forward, it is essential that the public and private sectors continue to have collaborative conversations about how to achieve the most out of big data technologies while deliberately applying these tools to avoid—and when appropriate, address—discrimination.
