Showing posts with label IT. Show all posts
Showing posts with label IT. Show all posts

Social & Organizational Success With Data



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.

Business success in the digital age will require organizations to take bold actions, including inventing new business models and changing the way they function. By 2017, 70% of successful digital business models will rely on deliberately unstable processes designed to shift with customers' needs.

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


There are many new technologies that enable the capture and management of the data flood. Your new data landscape should be a mix of these technologies, chosen to provide the right solution in terms of cost, flexibility and speed to suit each specific data set and meet the insight needs of the business.


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

Data science unlocks insights. Appreciating and understanding how value is derived from data needs to become part of the culture of the organization. Only by embedding it throughout the enterprise, and systematically making all decisions better informed, can organizations achieve the transformation to becoming insights-driven.


Principle 5: 
Unleash data- and insights-as-a-service

The demand from business users for information and data-driven insights is ever increasing across all organizations. To harness this, business users must feel that they can rapidly access the insights they need where and when they need it. Setting up a powerful platform that delivers these insights ‘on-demand’, is the ultimate goal.


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.




The Transforming Business Intelligence Landscape (2015 Update)

To compete in today's global economy, businesses and governments need agility and the ability to adapt quickly to change. And what about internal adoption to roll out enterprise-grade Business Intelligence (BI)applications?

BI change is ongoing; often, many things change concurrently.





One element that too often takes a back seat is the impact of changes on the organization's people. Prosci, an independent research company focused on organizational change management (OCM), has developed benchmarks that propose five areas in which change management needs to do better. They all involve the people side of change: better engage the sponsor; begin organizational change management early in the change process; get employees engaged in change activities; secure sufficient personnel resources; and better communicate with employees.

Five Plus One

Because BI is not a single application — and often not even a single platform — we recommend adding a sixth area: visibility into BI usage and performance management of BI itself, aka BI on BI. Forrester recommends keeping these six areas top of mind as your organization prepares for any kind of change.
Some strategic business events, like mergers, are high-risk initiatives involving major changes over two or more years; others, such as restructuring, must be implemented in six months. In the case of BI, some changes might need to happen within a few weeks or even days. All changes will lead to either achieving or failing to achieve a business.

Triggers for Change

There are seven major categories of business and organizational change:
  1. People acquisitions
  2. Technology acquisitions
  3. Business process changes
  4. New technology implementations
  5. Organizational transformations
  6. Leadership changes
  7. Changes to business process outsourcing or technology sourcing
When organizations decide to implement change, detailed upfront planning puts the framework in place to make that change a success. Project and change management teams work in parallel but have many points of intersection. Project managers focus on aspects like tasks, timelines, and technology, while change managers look at which people will be affected by the change and plan ways to mitigate fear. With any change, no matter how small, do not neglect the people angle by focusing only on the project management aspects of the change. Prosci warns that ignoring the people side of change until employees greet a go-live date with outrage and resistance will result in teams having to go back to the drawing board and rework, redesign, re-evaluate, and revisit the entire effort.

Flex Your Business Muscle

In the modern world, one cannot leave technology to technologists. In BI, this is especially challenging and critical with the added complexity of increased business involvement. Unlike other enterprise technology applications, where business and technology partner, in BI the business owns many BI components and work streams. In the world of BI, technology is everyone's job. Therefore, pay particular attention to several ways in which project leaders differ from change leaders:
  • Project leaders focus on tasks; change leaders focus on people.
  • Project leaders and change leaders work together to integrate their plans.
  • Change leaders are "people persons" with credibility in the organization.
  • Team members have a wide range of competencies but add additional value with a specialty.
  • BI projects are iterative; BI change management is constant and ongoing.
Meticulous preparation for BI change is critical to success. This means creating an awareness of the need for and value of change management. Management often underestimates the effort and resources necessary to implement the change. The end result or business outcome of the change must be explicit and clearly communicated to employees, customers, and partners. Planning includes specific tasks and activities, as well as careful analysis of people management challenges and how to address them.
Also, consider that BI projects are different from many technology projects and therefore require special change management considerations. 

In our recently published report we researched the following key areas of BI organizational change management

  • Identifying who will help make the change
  • Securing a budget to fund and support ongoing change management activities
  • Reaching out to specialists (we reviewed OCM capabilities of top management consulting firms
  • Making a change management plan
  • Preparing a varied, ongoing communications plan
  • Developing learning, development and an incentive plan
  • Planning for measuring change management effectiveness

Forecast & Trends in Business Intelligence for 2013


                  A BIG (Business Intelligence Growth) Year :)



Defining Business Intelligence in 2013 as A Collaborative Experience and a Shared Exercise of Asking and Answering Insightful Questions About a Business
 
 

             What a year 2012 was for business intelligence! The said old world of databases is developing faster and faster, with startups addressing new data problems and established companies innovating on their platforms. Web-based analytics tools are connecting to web-based data. And everything’s mobile.
 


         With all the attention organizations are placing on innovating around data, the rate of change will only increase. So what should you expect to see?

 


     Proliferation of Data Stores.

              Once upon a time, an organization had different types of data: CRM, point of sale, email, and more. The rulers of that organization worked very hard and eventually got all their data into one fast data warehouse…2013 is the year we will recognize this story as a fairy tale. The organization that has all its data in one place does not exist. Moreover, why would you want to do it? Big data could be in places like Teradata and Hadoop. Transactional data might be in Oracle or SQL Server. The right data stores for the right data and workload will be seen as one of the hallmarks of a great IT organization, not a problem to be fixed. 





                                Hadoop is Real.

                    Back in 2008 and 2009 Hadoop was a science project. By 2010 and 2011 some forward-thinking organizations started doing proof-of-concepts with Hadoop. In 2012, we saw the emergence of many production-scale Hadoop implementations, as well as a crop of companies trying to address pain points in working with Hadoop. In 2013, Hadoop will finally break into the mainstream for working with large or unstructured data. It is also becoming more “right-time” for a faster analytics experience. 



             Self-reliance Is the New Self-Service.


                Self-service BI is the idea that any business user can analyze the data they need to make a better decision. Self-reliance is the coming of age of that concept: it means business users have access to the right data, that the data is in a place and format that they can use, and that they have the solutions that enable self-service analytics. When all this happens, people become self-reliant with their business questions and IT can focus on providing the secure data and solutions to get them there. 




The Value of text and other Unstructured Data  is (finally!) Recognized.

                One of the subplots of the rise of Hadoop has been the rise of unstructured data. Emails, documents, web analytics and customer feedback have existed for years, but most organizations struggled enough to understand their structured data that unstructured data was left alone. In 2011 and 2012 we saw more techniques emerge to help people deal with unstructured data, not least of which is a place to put it (Hadoop). With the explosion of social data like Twitter and Facebook posts, text analysis becomes even more important. Expect to see a lot of it in 2013. 





                             Cloud BI Grows up.


               Cloud business intelligence as your primary BI? No way! Not in 2012, at least. There are cloud BI services, but with important limitations that have made it difficult to use the cloud as your primary analytics solution. In 2013 we expect to see the maturation of cloud BI, so that people can collaborate with data in the cloud, just like they collaborate on their Salesforce.




               Visual Analytics wins Best Picture.


              For years visual analytics has been the Best Documentary of business intelligence: impressive, but for the intellectuals and not the mass audience. But people are finally beginning to realize that visual analytics helps anyone explore, understand and communicate with data. It’s the star of business analytics, not a handy tool for scientists. 




Forecasting and Predictive Analytics become common.


                Much like visual analytics, forecasting used to be seen as the domain of the scientist. But everyone wants to know the future. Forecasting tools are maturing to help businesses identify emerging trends and make better plans. We expect forecasting and predictive analyses to become much more common as people use them to get more value from their data. 




              Mobile BI Moves up a Weight Class.


            Last year we predicted that Mobile BI would go mainstream—and it did. Now everyone from salespeople to insurance adjusters to shop floor managers use tablets to get data about their work right in the moment. To date mobile BI has been lightweight—involving the consumption of reports, with a bit of interactivity. But the tremendous value that people have seen in mobile BI is driving a trend for more ability to ask and answer questions. 





        Collaboration is not a Feature, it’s a Reality!

            Business intelligence solutions have often talked up their collaboration features. In 2013, that’ll no longer be good enough. Collaboration must be at the root of any business intelligence implementation, because what is business intelligence but a shared experience of asking and answering questions about a business? In 2013, business will look for ways to involve people all around their organization in working together to understand and solve problems. 



         Pervasive Analytics: Finally…Pervasive.

As an industry, we’ve talked for years about terms like “pervasive BI” or “BI for the masses”. There’s a whole market for data that is outside of the market for “business intelligence.” When we talk more about data, and less about software categories like BI, we get to the crux of maximizing business value—and fast, easy-to-use visual analytics is the key that opens the door to organization-wide analytics adoption and collaboration. 


            These are the trends we see in talking with customers about what they’re doing today and where we are investing for the future. The good news is that investment is most often being driven by a desire to take good initiatives farther, not a sense of frustration with failed initiatives. Perhaps the new technology and investment of the last few years is finally starting to pay off. No matter what, you can expect lots of change in business intelligence in 2013.