The Position of the CDO in the Organization

Many companies over the past few years have developed the position of Chief Data Officer (CDO), often placing that position in the C-suite. However, exact organizations vary, as one would expect, since organizations vary in their need for data management.

A common aspect of data management in modern business, though, is that data, including big data, along with the infrastructure needed to handle and analyze and, most importantly, put it to good strategic use, is a growing and even critical phenomenon.

Nevertheless there has been debate about whether CDO's should even exist, whether they should report to the CIO, CFO, or the CEO. The roles of the CIO (chief information officer) and CDO have been evolving, particularly in terms of their relationship to the strategic aspects of the business.

It has been obvious that technology has pervaded all aspects of business over recent years. That being the case, the activities of the CIO directly affect all strategic and operational areas of the business. Accordingly some activities that at one time were traditionally CIO activities have been spun off into the functional areas, with the CIO acting as coordinator. At the same time, the CIO has assumed more of a strategic role, working with various functional areas, such as marketing and finance.

Along comes the CDO, with a knowledge of data management, data sources, data infrastructure needs and the importance of data to particular functional areas. Clearly the CDO needs to operate at a strategic level in cooperation with the affected functional areas. One challenge, therefore, is to avoid duplication of effort.

To some extent duplication is not an issue, since the CDO is focused on the data more than the infrastructure and the CIO is more focused on the infrastructure rather than the data. There is a role for both, therefore, at the C-suite table, but their roles need to be clearly defined. And they need to work together.

To have the CDO report to the CIO will likely take away some of the benefits to the organization of advanced data management, so both should report to the CEO.

According to a recent report by the Gartner Group, this is the dominant trend.

An Era of Machine Intelligence

Gartner Group recently released their annual list of the top ten strategic trends in business. These trends focus on new technologies and how they are being used. They tell us a lot about how technology is shaping our world.

One of the major underlying themes in the list they produced this year is that of intelligence. It is clear that your technology is going to be much more intelligent in future and be able to interact with users on a much more intelligent basis.

By intelligent, they mean an ability to receive input and draw conclusions from it, make decisions and recommend courses of action.

Artificial intelligence plays a big role in this trend and is already being embraced by many companies. So we will see smarter devices in the world of the Internet of Things, as well as smart apps that can make decisions on the basis of data and other input. With the growing usage of oral input to computers, where it is predicted that in the relatively near term, most interaction with computers will be oral, combined with the greater intelligence of computers, we will see the prospect of being able to discuss problems with our computers and come to better decisions. Add in virtual reality and it is possible to foresee an ability to interact in such a way that all sensory perception is used to help in analyzing data, something that could be a big help in coping with big data.

As always, there are pros and cons of these developments. Technology has already replaced a lot of jobs but smarter computers will be able to replace a lot more. So far, most of the jobs replaced have been of the menial variety - repetitious and simple. However, as the intelligence of computers grows, more complex jobs ill be replaced, including some of the traditional professions, like accounting, architecture, many aspects of law and even medicine. But new professions are being created. We already see a strong demand for data analysts. And we are beginning to see a demand for machine trainers - devoted to providing direction to that intelligence.

The question one has to ask is - when will computers be given the right to vote (sic)??

Next Generation Technologies for Mobile Commerce

Mobile Commerce has been one of the fastest growing aspects of electronic commerce. It involves the use of smart phones, tablets and other mobile devices to conduct commercial transactions.

While mobile commerce has been growing, often smaller companies are unaware of the potential for enhanced marketing techniques or if aware, are fearful that such techniques will require too much of an investment in technical IT resources.

Enhanced marketing techniques depend on adopting mobile apps that enable some interaction with the customers, such as trying out products in some way, perhaps on a virtual basis, or enabling location based services to gain certain benefits.

The idea is that the mobile devices generate data that can be revealing about the actions and preferences of the customer.

Collecting such data is becoming easier with the development of new apps that involve some such interaction.  However, the issue that arises is how to make use of these data. That's where big data analytics, particularly predictive analytics.

The ability to implement predictive analytics on big data gathered from a wide variety of mobile devices is something that takes some preparation but is becoming increasingly important to the success of mobile e-commerce and therefore to the success of most companies selling products to the public.

Customers will expect marketing efforts directed their way to be customized to their needs and habits, and this is only possible by having the company adopt mobile apps that can collect big data and develop the ability to use analytics to generate those customized marketing techniques.

Frost and Sullivan recently released a useful research report titled "Next Generation Technologies for Mobile E-commerce" which explores these issues and provides some useful guidance. Some interesting summaries of this report can be found at Information Week and on the Frost and Sullivan website.

Mobile Commerce, big data analytics and customized marketing are coming together to create important changes in customer relations.

Scotland to Start Stock Exchange Using Blockchain

Scotex, The Scottish Stock Exchange, is seeking to raise as much as 15 million pounds ($18.4 million) to start a new regulated equity market next year, according to a statement on Thursday. Executed trades will be processed by Blockchain, the distributed-ledger technology that drives bitcoin. For more on this, click here. And here.

Needing an Infrastructure for Big Data

Anytime there is need to handle data, there is a need for an underlying infrastructure. The data needs to be gathered, organized, stored, processed, and used. In the case of big data, a particular infrastructure is needed, and it often involves tools like Hadoop, Hive, Spark and visualization software. The basic elements are the same as any data, but the tools and capacity are different. Without a proper infrastructure, big data will lose some or all of it value.  Some more detailed thoughts can be found in this article.

DDD - The Move Away From Intuition

Data Driven Decision making is taking hold in large companies. Not all, but many. This according to a survey carried out by Harvard, Stanford and the US Census Bureau.

Companies have access to more data than at any time in history. They are learning to use it and finding that the quality of the decisions to be better than the largely intuitive decisions of the past. This applies in manufacturing, marketing and other areas. There is a growing preference in many organizations to make fact-based decisions rather than those based just on opinon. For a summary of this research, check this link.

The New/Old Role of Libraries

New research into the nature and role of libraries confirms that libraries are here to stay as a vital part of our communities and that they are changing forever. The days of libraries being built around rooms full of books are drawing to a close. The new libraries feature digital reading, classes in technology use, places to do research using computers and safe havens for study and contemplation.

A majority of survey respondents believe that libraries need to emphasize the use of technologies, like computers, tablets and smartphones. They include the use of 3-D printers among the skills that libraries should teach. They also believe that libraries can play a role of helping people to learn what information they can rely on, something increasingly difficult in this age of social media. Follow this link for the report on libraries 2016.

Free Predictive Analytics Software

Predictive Analytics is an increasingly useful tool for business to assess risks and opportunities in some organized fashion. It doesn't necessarily provide answers but can often offer useful insights. Free software can provide a inexpensive but potentially useful way to venture into the world of predictive analytics. Here is a summary of some of the more popular free software available, such as R, Orange, Rapidminder, Anaconda and others.

The Basics of Predictive Analytics

For those who are getting into predictive analytics, a useful article by Eric Seigel, founder of Predictive Analytics World, provides a number of definitions that are useful to understanding the basics of predictive analytics. Many applications of predictive analytics have emerged in recent years, including in auditing, finance, marketing and others. Some effort is required, such as reading , taking courses, etc, to gain a level of knowledge that enables useful applications. However, knowing the definitions of such terms as Predictive analytics itself, predictive models, uplift models, vast search and others is a starting point. The article can be found through this link.

How to Develop Your Data Analytics Skills

Data analytics is becoming important to established professionals in many fields. Others are viewing it as a new career opportunity. Indeed the demand for data analytics professionals is strong and growing. There are many opportunities to  develop or improve skills, including back to school, online courses, technical training, networking and the like. This recent article sets out a number of options and provides examples.

The Sharing, Collaborative and Gig Economy

Pew Research has released an interesting study on the new App-based Economy which reveals a number of splits in population usage of the new services. They focused primarily on ride-hailing, home sharing and crowdfunding apps. Issues explored involve jobs, regulation and the potential of a new digital divide. Uber, AirBNB and Kickstarter.

All are used by a low percentage of adults, and most have never heard of these apps. As one would expect, most users are relatively young - under 45 - and all of the services are evolving with new regulations.

The new digital economy is a permanent part of the landscape, and such research is fundamentally important for business and policy-makers.

See the report here.

Lack of Skilled Staff Inhibits Adoption of IoT

A recent report by Cisco and Capita states that IT leaders are concerned that they do not have enough skilled resources to carry out potential IoT adoptions. This is an issue in several important and new areas of IT Management. It points to a need to change the approach to IT Education and certification, perhaps to one of emphasizing cooperative training with industry rather than gaining university degrees.

University degrees take a long time and the people acquiring them have little or no experience. Cooperative programs with trade schools and community colleges are faster and produce people with important and useful on-the-job experience. Check out this short summary of the report.

IoT Connections to Exceed Mobile Phones

The Internet of Things has been growing exponentially. A recent report by Ericsson finds that by 2018, the number of IoT connections will exceed that of mobile phones.

This growth is generated by such phenomena as smart houses, transport logistics, medical applications, autonomous cars and remote manufacturing.

Many of these applications are highly sensitive and require a very high degree of reliability and stability. Therefore they are a focus of IT management and a drain on  resources. With a projected annual growth rate of 23%, such IoT devices are becoming ubiquitous.

For a rundown on the Ericsson report, check this link.

Growing Volumes of Data Create Management Issues

The world supply of data is growing rapidly. That's no secret. At present the global volume of data is 4.4 zettabytes. One Zettabyte equals one sextillion bytes or 10 to the 16th power or one trillion gigabytes. It's hard to imagine, but clearly that's a lot of data. Predictions by IDC in a recent study indicate that global data will increase from 4.4 to 44 zettabytes over the next four years. Business will bear the brunt of this growth in data and will need to manage it.

The cloud is crucial to managing the data. But there also needs to be an organized and efficient means of integrating the data with corporate systems, including ERP and other enterprise systems. An additional complication is that much of the ultimate output needs to be channeled to mobile devices, including smartphones. There is a growing realization that the mobile world in which we live is an ongoing reality and the primary means of consuming information for the foreseeable future.

While systems integration has been with us for many years, the integration process involved with the new data is a relatively new field. It must take place in the cloud first and that primarily involves organizing the data so it can be processed. Companies are using "data lakes" for the first stage and then transitioning to cloud based data warehouses and various applications that feed into mobile apps.

For an interesting overview of the processes, here's a good article on the subject.

The Sharing Economy Grows

In May, Pew Research released a report on its surveys about various key aspects of the sharing economy. They covered ride sharing, home sharing and crowdfunding.  The results are interesting. they show that a large proportion of adults have used ride sharing (like Uber) and find it a satisfactory (or better) experience. Most are young urban adults. Fewer use home sharing and there is a common concern expressed among users or potential users about safety. Even fewer have used crowdfunding, but its use is growing.  For the reports, follow this link.

How to Make your Company Data-driven

Optimum use of data in management is rapidly shifting from competitive advantage to competitive necessity. But implementing an effective data use policy can result in spending massive amounts of money with poor results if it is not done well.

Serious planning is a necessity. As with any planning activity, definition of objectives is crucial, including specific definition of strategic and operational business objectives, and identification of data that will meet these objectives.

There are massive amounts of data on the internet, but not all are readily available for consumption. This is a significant consideration. Also, if you are planing to use internet based data, don't even think about developing your own systems to handle it. It should be capable of being analyzed without downloading. There are numerous tools available in the cloud where online cloud-based analysis can be carried out.

Ensure the results of the analysis are placed in the right hands. This would have been planned in the objectives identification phase. But you need to make sure that the results are configured and visualized in ways that make good use of graphs and other visuals or pictorials. Often the recipients are busy and won't take the time to spend a additional time on the analysis unless there is a rapid gratification.

This leads to another point about presenting the results. It must be done with a minimum of disruption to the recipients normal routines.  Again, this approach will lead to better use of the analysis.

For additional thoughts on this, and a source for this posting, click this link.

Job Automation - The big Trend of the 21st Century

People who do physical jobs have been faced with job losses because of automation for several decades now. The trend has been accelerating with the automation of entire factories and the need of retrain millions of workers.

Now the automation trend is spreading into the professions, even though their jobs are often not physical. At risk are accountants, lawyers, architects, to name a few. The automation of these fields does not mean that all will lose their jobs, but it does mean that large parts of their jobs can be automated, leading to a lower demand for them. For example a recent major report for the Canadian Bar Association called "Futures: Transforming the Delivery of Legal Services in Canada that outlined a strategy to deal with transformative change in the delivery of legal services. One of the driving forces for the change was technological innovation.

Last year, CPA Canada released a report setting out Drivers of Change in the accounting profession, with a strong emphasis on technological change. Other writers have written about how mobile devices, social media and data analytics will change the profession.

Behind the rapid spread of automation into the professions is the growing power of artificial intelligence. This enables technology to replace or significantly modify jobs with more and more intellectual content.

The Pew Research Center recently did a major survey into the views of a wide spectrum of professions on this issue. The results are quite revealing.

Mobile Brings Data to Real Time Decision Making

The increasing us of mobile smartphones and tablets is making it possible to generate data in the field and transmit it directly for analysis. In one example recently cited where such an approach was used for helping refugees,  mobile data, including GPS data and the results of interviews were collected and sent directly to a center using ESRI analytics tools.

There, the data could be immediately visualized, analyzed and used for ongoing decisions.

"With ESRI software, we were able to visualize what was going on,'' said Andrew Schroeder, director of research and analysis at Direct Relief, a Santa Barbara, Calif., nonprofit organization that coordinates humanitarian aid and care for people in poverty or emergencies, noting that ArcGIS's mapping capabilities "allowed us to understand some of the dynamics around issues such as who seeks care, where they are from and what their neighborhood conditions are."

For more on this experience with real time data analytics, check out this article.

How Big Data can Enable Targeted Marketing

Traditional marketing has used the broad broom approach, where advertisements are placed on media for general consumption, such as TV, Newspapers, etc. However, these media are attracting a smaller cohort than they did and therefore the ads miss their targets. Also, advertising on broad based media has become very expensive. The best example of this is the massive millions that are spend on superbowl ads.

Many of the people who are missed through the broad based marketing are now using hand held devices, such as smart phones and tablets. This applies to many younger people but is not limited to them. So advertising campaigns need to be segmented so that multiple campaigns can be run simultaneously that are directed to those segments.

This is where big data comes in. Data can be obtained from social media networks and internet usage data that show where and when the target audiences are using their media. Such advertising can then be much more effective in reaching audiences and promoting sales.

This article shows how targeted marketing techniques can be used in election campaigns and what business can learn from politics. Check it out.

Big Data for Cybersecurity

The growing use of big data has definite links to the problem of cybersecurity. Contrary to what many might think, the linkage is not all negative. While the risks in use of big data need to be controlled as do any other IT application, nevertheless big data can be useful in helping to control that risk. For example, predictive analytics can be applied to a combination of historical data and statistical metadata to give enterprises the ability to predict the probability of an intrusive event happening in the future. Also, big data analytics enables the data to be retained in its original form, whether structured or unstructured, thus providing much more flexibility in the analytical process. For a good rundown on this area, click this link.