Myles Suer

Myles Suer
Mr. Suer is a senior manager of solutions marketing at Informatica Corporation. Much of Mr. Suer’s experience has been as a BI practitioner. At HP and Peregrine, Mr. Suer led the product management team applying BI and Scorecard technology to these company’s IT management products. Prior to HP, Mr. Suer led new product initiatives at start-ups and large companies. This included doing a restart of a Business Activity Monitoring Company. Mr. Suer has, also, been a software industry analyst. Mr. Suer holds a Master of Science degree from UC Irvine and a 2nd Masters in Business Administration in Strategic Planning from the University of Southern California.

Is it the CDO or CAO or Someone Else?

Frank-Friedman-199x300A month ago, I shared that Frank Friedman believes CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”. Even though many CFOs are increasingly taking on what could be considered an internal CEO or COO role, many readers protested my post which focused on reviewing Frank Friedman’s argument. At the same time, CIOs have been very clear with me that they do not want to personally become their company’s data steward. So the question becomes should companies be creating a CDO or CAO role to lead this important function? And if yes, how common are these two roles anyway?

Data analyticsRegardless of eventual ownership, extracting value out of data is becoming a critical business capability. It is clear that data scientists should not be shoe horned into the traditional business analyst role. Data Scientists have the unique ability to derive mathematical models “for the extraction of knowledge from data “(Data Science for Business, Foster Provost, 2013, pg 2). For this reason, Thomas Davenport claims that data scientists need to be able to network across an entire business and be able to work at the intersection of business goals, constraints, processes, available data and analytical possibilities. Given this, many organizations today are starting to experiment with the notion of having either a chief data officers (CDOs) or chief analytics officers (CAOs). The open questions is should an enterprise have a CDO or a CAO or both? And as important in the end, it is important to determine where should each of these roles report in the organization?

Data policy versus business questions

Data analyticsIn my opinion, it is the critical to first look into the substance of each role before making a decision with regards to the above question. The CDO should be about ensuring that information is properly secured, stored, transmitted or destroyed.  This includes, according to COBIT 5, that there are effective security and controls over information systems. To do this, procedures need to be defined and implemented to ensure the integrity and consistency of information stored in databases, data warehouses, and data archives. According to COBIT 5, data governance requires the following four elements:

  • Clear information ownership
  • Timely, correct information
  • Clear enterprise architecture and efficiency
  • Compliance and security

Data analyticsTo me, these four elements should be the essence of the CDO role. Having said this, the CAO is related but very different in terms of the nature of the role and the business skills require. The CRISP model points out just how different the two roles are. According to CRISP, the CAO role should be focused upon business understanding, data understanding, data preparation, data modeling, and data evaluation. As such the CAO is focused upon using data to solve business problems while the CDO is about protecting data as a business critical asset. I was living in in Silicon Valley during the “Internet Bust”. I remember seeing very few job descriptions and few job descriptions that existed said that they wanted a developer who could also act as a product manager and do some marketing as a part time activity. This of course made no sense. I feel the same way about the idea of combining the CDO and CAO. One is about compliance and protecting data and the other is about solving business problems with data. Peanut butter and chocolate may work in a Reese’s cup but it will not work here—the orientations are too different.

So which business leader should own the CDO and CAO?

Clearly, having two more C’s in the C-Suite creates a more crowded list of corporate officers. Some have even said that this will extended what is called senior executive bloat. And what of course how do these new roles work with and impact the CIO? The answer depends on organization’s culture, of course. However, where there isn’t an executive staff office, I suggest that these roles go to different places. Clearly, many companies already have their CIO function already reporting to finance. Where this is the case, it is important determine whether a COO function is in place. The COO clearly could own the CDO and CAO functions because they have a significant role in improving process processes and capabilities. Where there isn’t a COO function and the CIO reports to the CEO, I think you could have the CDO report to the CIO even though CIOs say they do not want to be a data steward. This could be a third function in parallel the VP of Ops and VP of Apps. And in this case, I would put the CAO report to one of the following:  the CFO, Strategy, or IT. Again this all depends on current organizational structure and corporate culture. Regardless of where it reports, the important thing is to focus the CAO on an enterprise analytics capability.

Related Blogs

Should we still be calling it Big Data?

Is Big Data Destined To Become Small And Vertical?

Big Data Why?

What is big data and why should your business care?

Author Twitter: @MylesSuer

Share
Posted in Big Data, CIO | Tagged , , , , , , | Leave a comment

Major Oil Company Uses Analytics to Gain Business Advantage

analytics case studies-GasAccording Michelle Fox of CNBC and Stephen Schork, the oil industry is in ‘dire straits’. U.S. crude posted its ninth-straight weekly loss this week, landing under $50 a barrel. The news is bad enough that it is now expected to lead to major job losses. The Dallas Federal Reserve anticipates that the Texas could lose about 125,000 jobs by the end of June. Patrick Jankowski, an economist and vice president of research at the Greater Houston Partnership, expects exploration budgets will be cut 30-35 percent, which will result in approximately 9,000 fewer wells being drilled. The problem is “if oil prices keep falling, at some point it’s not profitable to pull it out of the ground” (“When, and where, oil is too cheap to be profitable”, CNBC, John W. Schoen). job losses 

This means that a portion of the world’s oil supply will become unprofitable to produce. According to Wood Mackenzie, “once the oil price reaches these levels, producers have a sometimes complex decision to continue producing, losing money on every barrel produced, or to halt production, which will reduce supply”. The question are these the only answers?

Major Oil Company Uses Analytics to Gain Business Advantage

analytics case studiesA major oil company that we are working with has determined that data is a success enabler for their business. They are demonstrating what we at Informatica like to call a “data ready business”—a business that is ready for any change in market conditions. This company is using next generation analytics to ensure their businesses survival and to make sure they do not become what Jim Cramer likes to call a “marginal producer”.  This company has said to us that their success is based upon being able to extract oil more efficiently than its competitors.

Historically data analysis was pretty simple

analytics case studies-oil drillingTraditionally oil producers would get oil by drilling a new hole in the ground.  And in 6 months they would start getting the oil flowing commercially and be in business. This meant it would typically take them 6 months or longer before they could get any meaningful results including data that could be used to make broader production decisions.

Drilling from data

Today, oil is, also, produced from shale or fracking techniques.  This process can take only 30-60 days before oil producers start seeing results.  It is based not just on innovation in the refining of oil, but also on innovation in the refining of data from operational business decisions can be made. The benefits of this approach including the following:

Improved fracking process efficiency

analytics case studies-FrackingFracking is a very technical process. Producers can have two wells on the same field that are performing at very different levels of efficiency. To address this issue, the oil company that we have been discussing throughout this piece is using real-time data to optimize its oil extraction across an entire oil field or region. Insights derived from these allow them to compare wells in the same region for efficiency or productivity and even switch off certain wells if the oil price drops below profitability thresholds. This ability is especially important as the price of oil continues to drop.  At $70/barrel, many operators go into the red while more efficient data driven operators can remain profitable at $40/barrel.  So efficiency is critical across a system of wells.

Using data to decide where to build wells in the first place

When constructing a fracking or sands well, you need more information on trends and formulas to extract oil from the ground.  On a site with 100+ wells for example, each one is slightly different because of water tables, ground structure, and the details of the geography. You need the right data, the right formula, and the right method to extract the oil at the best price and not impact the environment at the very same time.

The right technology delivers the needed business advantage

analytics case studiesOf course, technology is never been simple to implement. The company we are discussing has 1.2 Petabytes of data they were processing and this volume is only increasing.  They are running fiber optic cables down into wells to gather data in real time. As a result, they are receiving vast amounts of real time data but cannot store and analyze the volume of data efficiently in conventional systems. Meanwhile, the time to aggregate and run reports can miss the window of opportunity while increasing cost. Making matters worse, this company had a lot of different varieties of data. It also turns out that quite of bit of the useful information in their data sets was in the comments section of their source application.  So traditional data warehousing would not help them to extract the information they really need. They decided to move to new technology, Hadoop. But even seemingly simple problems, like getting access to data were an issue within Hadoop.  If you didn’t know the right data analyst, you might not get the data you needed in a timely fashion. Compounding things, a lack of Hadoop skills in Oklahoma proved to be a real problem.

The right technology delivers the right capability

The company had been using a traditional data warehousing environment for years.  But they needed help to deal with their Hadoop environment. This meant dealing with the volume, variety and quality of their source well data. They needed a safe, efficient way to integrate all types of data on Hadoop at any scale without having to learn the internals of Hadoop. Early adopters of Hadoop and other Big Data technologies have had no choice but to hand-code using Java or scripting languages such as Pig or Hive. Hiring and retaining big data experts proved time consuming and costly. This is because data scientists and analysts can spend only 20 percent of their time on data analysis and the rest on the tedious mechanics of data integration such as accessing, parsing, and managing data. Fortunately for this oil producer, it didn’t have to be this way. They were able to get away with none of the specialized coding required to scale performance on distributed computing platforms like Hadoop. Additionally, they were able “Map Once, Deploy Anywhere,” knowing that even as technologies change they can run data integration jobs without having to rebuild data processing flows.

Final remarks

It seems clear that we live in an era where data is at the center of just about every business. Data-ready enterprises are able to adapt and win regardless of changing market conditions. These businesses invested in building their enterprise analytics capability before market conditions change. In this case, these oil producers will be able to produce oil at lower costs than others within their industry. Analytics provides three benefits to oil refiners.

  • Better margins and lower costs from operations
  • Lowers risk of environmental impact
  • Lower time to build a successful well

In essence, those that build analytics as a core enterprise capability will continue to have a right to win within a dynamic oil pricing environment.

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer

Share
Posted in Big Data, CIO, Data Quality | Tagged , , , | Leave a comment

Should We Still be Calling it Big Data?

shutterstock_227687962Several months ago, I was talking to some CIOs about their business problems. During these conversations, I asked them about their interest in Big Data. One sophisticated CIO recoiled almost immediately saying that he believes most vendors are really having a problem discussing “Big Data” with customers like him. It would just be so much easier if you guys would talk to me about helping my company with our structured data and unstructured data. At the same time, Gartner has found that 64% of enterprises surveyed say they’re deploying or planning to deploy a Big Data project. The problem is that 56% of those surveyed by Gartner are still struggling to determine how to get value out of big data projects and 23% are struggling with the definition of what is Big Data and what is not Big Data.

DavenportClearly, this says the term does not work with market and industry participants. To me this raises a question about the continued efficacy of the term. And now, Thomas Davenport, the author of “Competing on Analytics”, has suggested that we retire the term all together. Tom says that in his research “nobody likes the term”. He claims in particular that executives yearn for a better way to communicate what they are doing with data and analytics.

Tom suggests in particular that “Big Data” has five significant flaws:

1)      Big is relative. What is big today will not be so large tomorrow. Will we have to tall call the future version Big Big Data?

2)      Big is only one aspect of what is distinctive about the data in big data. Like my CIO friend said it is not as much about the size of data as it is about the nature of the data. Tom says bigness demands more powerful services, but a lack of structure demands different approaches to process the data.

3)      Big data is defined as having volume, variety, and velocity. But what do you call data that has variety and velocity but the data set is not “big”.

4)      What do you call the opposite of big data? Is it small data? Nobody likes this term either.

5)      Too many people are using “big data” incorrectly to mean any use of analytics, reporting, or conventional business intelligence.

QuestionTom goes onto say, “I saw recently, over 80 percent of the executives surveyed thought the term was overstated, confusing, or misleading”. So Tom asks why don’t we just stop using it. In the end, Tom struggles with ceasing his use of the term because the world noticed the name Big Data unlike other technological terms. Tom has even written a book on the subject—“Big Data at Work”. The question I have is do we in the IT industry want to really lose all the attention. It feels great to be in the cool crowd. However, CIOs that I have talked to say they are really worried about what will happen if their teams oversell Big Data and do not deliver tangible business outcomes. The reality Tom says it would be more helpful than saying, we are cool and we are working on big data to instead say instead we’re extracting customer transaction data from our log files in order to help marketing understand the factors leading to customer attrition”. I tend to agree with this thought but I would like to hear what you think? Should we as an industry retire the term Big Data?

Related links

Related Blogs

Is Big Data Destined To Become Small And Vertical?
Big Data Why?
What is big data and why should your business care?

Author Twitter: @MylesSuer

Share
Posted in Data Governance | Tagged , , | Leave a comment

Good Corporate Governance Is Built Upon Good Information and Data Governance

Good Corporate Governance

Good Corporate Governance

As you may know, COSO provides the overarching enterprise framework for corporate governance. This includes operations, reporting, and compliance. A key objective for COSO is the holding of individuals accountable for their internal control responsibilities. The COSO process typically starts by accessing risks and developing sets of control activities to mitigate discovered risks.

On an ongoing basis, organizations then need as well to generate relevant, quality information to evaluate the functioning of established internal controls. And finally they need to select, develop, and perform ongoing evaluations to ascertain whether the internal controls are present and functioning appropriately. Having said all of this, the COSO framework will not be effective without first having established effective Information and Data Governance.

So you might be asking yourself as a corporate officer why should you care about this topic anyway. Isn’t this the job of the CIO or that new person, the CDO? The answer is no. Today’s enterprises are built upon data and analytics. The conundrum here is that “you can’t be analytical without data and you can’t be really good at analytics without really good data”. (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 23). What enterprises tell us they need is great data—data which is clean, safe, and increasingly connected. And yes, the CIO is going to make this happen for you, but they are not going to do this appropriately without the help of data stewards that you select from your business units. These stewards need to help the CIO or CDO determine what data matters to the enterprise. What data should be secured? And finally, they will determine what data, information, and knowledge will drive the business right to win on an ongoing basis.

So now that you know why your involvement matters, I need to share that this control activity is managed by a supporting standard to COSO, COBIT 5. To learn specifically about what COBIT 5 recommends for Information and Data Governance, please click and read an article from the latest COBIT Focus entitled “Using COBIT 5 to Deliver Information and Data Governance”.

Twitter: @MylesSuer

Share
Posted in CIO, Data Governance | Tagged , , | Leave a comment

Analytics Stories: A Pharmaceutical Case Study

Pharma CIOAs I have shared within other posts within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. For pharmaceutical businesses, strengthening the right to win begins and ends with the drug product development lifecycle. I remember, for example, talking several years ago to the CFO of major pharmaceutical company and having him tell me the most important financial metrics for him had to do with reducing the time to market for a new drug and maximizing the period of patent protection. Clearly, the faster a pharmaceutical company gets a product to market, the faster it can begin to earning a return on its investment.

Fragmented data challenged analytical efforts

PharmaceuticalAt Quintiles, what the business needed was a system with the ability to optimize design, execution, quality, and management of clinical trials. Management’s goal was to dramatically shorten time to complete each trial, including quickly identifying when a trial should be terminated. At the same time, management wanted to continuously comply with regulatory scrutiny from Federal Drug Administration and use it to proactively monitor and manage notable trial events.

The problem was Quintiles data was fragmented across multiple systems and this delayed the ability to make business decisions. Like many organizations, Quintiles data was located in multiple incompatible legacy systems. This meant there was extensive manual data manipulation before data could become useful. As well, incompatible legacy systems impeded data integration and normalization, and prohibited a holistic view across all sources. Making matters worse, management felt that it lacked the ability to take corrective actions in a timely manner.

Infosario launched to manage Quintiles analytical challenges

PharmaceuticalTo address these challenges, Quintiles leadership launched the Infosario Clinical Data Management Platform to power its pharmaceutical product development process. Infosario breaks down the silos of information that have limited combining massive quantities of scientific and operational data collected during clinical development with tens of millions of real-world patient records and population data. This step empowered researchers and drug developers to unlock a holistic view of data. This improved decision-making, and ultimately increasing the probability of success at every step in a product’s lifecycle. Quintiles Chief Information Officer, Richard Thomas says, “The drug development process is predicated upon the availability of high quality data with which to collaborate and make informed decisions during the evolution of a product or treatment”.

What Quintiles has succeeded in doing with Infosario is the integration of data and processes associated with a drug’s lifecycle. This includes creating a data engine to collect, clean, and prepare data for analysis. The data is then combined with clinical research data and information from other sources to provide a set of predictive analytics. This of course is aimed at impacting business outcomes.

The Infosario solution consists of several core elements

At its core, Infosario provides the data integration and data quality capabilities for extracting and organizing clinical and operational data. The approach combines and harmonizes data from multiple heterogeneous sources into what is called the Infosario Data Factory repository. The end is to accelerate reporting. Infosario leverages data federation /virtualization technologies to acquire information from disparate sources in a timely manner without affecting the underlying foundational enterprise data warehouse. As well, it implements a rule-based, real-time intelligent monitoring and alerting to enable the business to tweak and enhance business processes as they are needed. A “monitoring and alerting layer” sits on top of the data, with the facility to rapidly provide intelligent alerts to appropriate stakeholders regarding trial-related issues and milestone events. Here are some more specifics on the components of the Infosario solution:

• Data Mastering provides the capability to link multi-domains of data. This enables enterprise information assets to be actively managed, with an integrated view of the hierarchies and relationships.

• Data Management provides the high performance, scalable data integration needed to support enterprise data warehouses and critical operational data stores.

• Data Services provides the ability to combine data from multiple heterogeneous data sources into a single virtualized view. This allows Infosario to utilize data services to accelerate delivery of needed information.

• Complex Event Processing manages the critical task of monitoring enterprise data quality events and delivering alerts to key stakeholders to take necessary action.

Parting Thoughts

According to Richard Thomas, “the drug development process rests on the high quality data being used to make informed decisions during the evolution of a product or treatment. Quintiles’ Infosario clinical data management platform gives researchers and drug developers with the knowledge needed to improve decision-making and ultimately increase the probability of success at every step in a product’s lifecycle.” This it enables enhanced data accuracy, timeliness, and completeness. On the business side, it has enables Quintiles to establish industry-leading information and insight. And this in turn has enables the ability to make faster, more informed decisions, and to take action based on insights. This importantly has led to a faster time to market and a lengthening of the period of patent protection.

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

Share
Posted in CIO, Data Governance, Data Quality | Tagged , , , , | Leave a comment

What Should Come First: Business Processes or Analytics?

business processesAs more and more businesses become fully digitized, the instantiation of their business processes and business capabilities becomes based in software. And when businesses implement software, there are choices to be made that can impact whether these processes and capabilities become locked in time or establish themselves as a continuing basis for business differentiation.

Make sure you focus upon the business goals

business processesI want to suggest that whether the software instantiations of business process and business capabilities deliver business differentiation depends upon whether business goals and analytics are successfully embedded in a software implementation from the start. I learned this first hand several years ago. I was involved in helping a significant insurance company with their implementation of analytics software. Everyone in the management team was in favor of the analytics software purchase. However, the project lead wanted the analytics completed after an upgrade had occurred to their transactional processing software. Fortunately, the firm’s CIO had a very different perspective. This CIO understood that decisions regarding the transaction processing software implementation could determine whether critical metrics and KPIs could be measured. So instead of doing analytics as an afterthought, this CIO had the analytics done as a fore thought. In other words, he slowed down the transactional software implementation. He got his team to think first about the goals for the software implementation and the business goals for the enterprise. With these in hand, his team determined what metrics and KPIs were needed to measure success and improvement. They then required the transaction software development team to ensure that the software implemented the fields needed to measure the metrics and KPIs. In some cases, this was as simple as turning on a field or training users to enter a field as the transaction software went live.

Make the analytics part of everyday business decisions and business processes

Tom DavenportThe question is how common is this perspective because it really matters. Tom Davenport says that “if you really want to put analytics to work in an enterprise, you need to make them an integral part of everyday business decisions and business processes—the methods by which work gets done” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). For many, this means turning their application development on its head like our insurance CIO. This means in particular that IT implementation teams should no longer be about just slamming in applications. They need to be more deliberate. They need to start by identifying the business problems that they want to get solved through the software instantiation of a business process. They need as well to start with how they want to improve process by the software rather than thinking about getting the analytics and data in as an afterthought.

Why does this matter so much? Davenport suggests that “embedding analytics into processes improves the ability of the organization to implement new insights. It eliminates gaps between insights, decisions, and actions” (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 121). Tom gives the example of a car rental company that embedded analytics into its reservation system and was able with the data provided to expunge long held shared beliefs. This change, however, resulted in a 2% increased fleet utilization and returned $19m to the company from just one location.

Look beyond the immediate decision to the business capability

Davenport also suggests as well that enterprises need look beyond their immediate task or decision and appreciate the whole business process or what happens upstream or downstream. This argues that analytics be focused on the enterprise capability system. Clearly, maximizing performance of the enterprise capability system requires an enterprise perspective upon analytics. As well, it should be noted that a systems perspective allows business leadership to appreciate how different parts of the business work together as a whole. Analytics, therefore, allow the business to determine how to drive better business outcomes for the entire enterprise.

At the same time, focusing upon the enterprise capabilities system in many cases will overtime lead a reengineering of overarching business processes and a revamping of their supporting information systems. This allows in turn the business to capitalize on the potential of business capability and analytics improvement. From my experience, most organizations need some time to see what a change in analytics performance means. This is why it can make sense to start by measuring baseline process performance before determining enhancements to the business process. Once completed, however, refinement to the enhanced process can be determined by continuously measuring processes performance data.

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

Share
Posted in B2B, B2B Data Exchange, Business Impact / Benefits, Business/IT Collaboration, Enterprise Data Management | Tagged , , , | Leave a comment

What is the Role of the CIO in Driving Enterprise Analytics?

Data AnalysisWhen you talk to CIOs today about their business priorities, the top of their list is better connecting what IT is doing to business strategy. Or put another way, it is about establishing business/IT alignment. One area where CIOs need to make sure there is better alignment is enterprise analytics. CIOs that I have talk to share openly that business users are demanding the ability to reach their apps and data anywhere and on any device. For this reason, even though CIOs say they have interest in the mechanisms of data delivery–data integration, data cleanliness, data governance, data mastering, and even metadata management — they would not take a meeting on these topics. The reason is that CIOs say they would need to involve their business partner in these meetings. CIOs these days want you have to have a business value proposition. Given this, CIOs say that they would want to hear about what the business wants to hear about.

  • Enabling new, valuable business insights out data to happen faster
  • Enabling their businesses to compete with analytics

CIOs as an analytics proponent versus the analytics customer

Tom DavenportSo if the question is about competing with analytics, what role does the CIO have in setting the agenda here? Tom Davenport says that CIOs–as I heard in my own conversations  with CIOs–have good intentions when it comes to the developing an enterprise information strategy. They can see the value of taking an enterprise versus a departmental view. Tom suggests, however, that CIOs should start by focusing upon the analytics that will matter most to the business. He says that IT organizations should, also, build an IT infrastructure capable of delivering the information and analytics that people across the enterprise need not just now but also in the future.

Tom says that IT organizations must resist the temptation to provide analytics as an add-on or a bolt-on basis for whatever transactions system have just been developed. As a product manager, I had a development team that preferred to add analytics by source rather than do the hard work of creating integrative measures that crossed sources. So I know this problem firsthand. Tom believes that IT needs to build a platform that can be standardized and integrate data from more than one source. This includes the ability to adapt as business needs and business strategies change.

Making this an Enterprise Analytics Capability

analyticsIn the early stage for analytics, IT organizations need to focus more upon a self-service approach. But as the business matures at analytics, Tom says that IT needs to shift gears and become a proactive advocate and architect of change. Tom says that IT should be a part owner of the company’s analytical capabilities. IT managers, therefore, must understand and be able to articulate the potential for analytics being created at an enterprise level. At the same time, the IT staff–which often lacks the heavy mathematical backgrounds of analysts–needs to be able to interact with the analytics pros who use and consume the information that IT creates to build models. I had this dilemma first hand where my analytics modelers were disconnected from BI product developers. They were two different communities working on our project. And although some modelers can build apps or even a BI system, what excites them most in life is building new analytical models.

Talk the language of the business

Tom Davenport says that IT managers can make their own lives easier with the business and the with analysts by instead of discussing cloud computing, service oriented architecture, or even OLAP, discussing decision making, insights, and business performance. Meanwhile, Tom feels that the enterprise analytics journey starts with good, integrated data on transactions and business processes managed through enterprise applications like ERP and CRM Systems (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 51).

Focusing on the big questions and the right problems

Clearly driving the business to focus on the big questions and the right problems is critical. IT cannot do this but they can facilitate it. Why does it matter? An Accenture Study found that “companies that derived any real value from them (their analytics) had anticipated how to leverage the information to generate new insights to improve business performance. (“Using Enterprise Systems to Gain Uncommon Competitive Advantage, Accenture, page 3). This is critical and too few organizations succeed in doing it.

With this accomplished and to achieve the second goal, IT needs to be eliminating legacy BI systems and old spaghetti code as well as silo data marts. The goal should be to replace them with an enterprise analytics capability that answers the big questions. This requires standardization around an enterprise wide approach that ensures a consistent approach to data management and provides an integrated environment complete with data repositories/data lakes, analytical tools, presentation applications, and transformational tools. This investment should be focused on improving business processes or providing data needed for system of systems products. Tom says that IT’s job is to watch out for current and future users of information systems.

Parting Thoughts

So the question is where is your IT organization at today? Clearly, it is important as well that IT measure enterprise analytic initiatives too. IT should measure adoption. IT should find out what is used or not they are used. I had a CIO once admit to me that he did not know whether currently supported data marts were being used or even still had value. It is important that we have these answers. Clearly, being close to the business customer from the start can limit what this CIO discussed.

Related Blogs and Links

Analytics Stories: A Banking Case Study

Analytics Stories: A Financial Services Case Study

Analytics Stories: A Healthcare Case Study

Who Owns Enterprise Analytics and Data?

Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR

Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

 

Share
Posted in Big Data, Business/IT Collaboration, CIO, Data Governance | Tagged , | Leave a comment

It is All About the “I”

Six ideas for CIOs in 2015 to put the innovation back in CIO

CIOFor most, the “I” in CIO stands for Information. But what about that other “I”, Innovation? For many IT organizations, 60-80% of IT spending continues to be tied up in keeping the IT lights on. But innovation matters more than ever to the business bottom line. According Geoffrey Moore, “without innovation, offerings become more and more like each other. They commoditize.” (“Dealing with Darwin”, Geoffrey Moore, page 1). Geoffrey goes on to say later in “Dealing with Darwin” that commoditization will over time drop business returns to “the cost of capital”. So clearly, this is a place that no CIO would want their enterprises to consciously go.

Given this, what is the role of the CIO in driving enterprise innovation? I believe that it is a significant one. Without question, technology investment has been a major driver of enterprise productivity gains. At the same time, IT investment has had a major role in improving business capabilities and the business value chains. And more recently, IT is even carving out a role in products themselves as part of the IoT. So how can CIOs help drive business innovation?

1) Get closer to your business customers. CIOs have said to me that their number one priority is connecting what the IT is doing to what the business is doing. Given this, CIOs should make it a real priority for their teams to get closer to the business this year. According to Kamalini Ramdas’ Article in Harvard Business Review, “to succeed at innovation, you need to have a culture in which everyone in the company is constantly scanning for ideas”.

2) Develop internal design partners. When I have started new businesses, I have always created a set of design partners to ensure that I built the right products. I tell my design partners to beat me up now rather than after I build the product. You need, as Kamalini Ramdas suggests, to harvest the best ideas of your corporate team just like I did with startups. You can start by focusing your attention upon the areas of distinctive capability—the places that give your firm its right to win.

3) Enabling your IT leaders and individual contributors to innovate. For many businesses, speed to market or speed of business processes can represent a competitive advantage. Foundationally to this are IT capabilities including up time, system performance, speed of project delivery, and the list goes on. Encouraging everyone on your team to drive superior operational capabilities can enable business competitive advantage. And one more thing, make sure to work with your business leaders to pass a portion of the business impact for improvements into a bonus for the entire enabling IT team. At Lincoln Electric, they used bonuses by team to continuously improve their products. This arch welding company shares the money saved from each process improvement with the entire team. They end up getting the best team and highest team longevity as teams work improves product quality and increases cost take out. According Kamalini, “in truly innovative culture, leaders need to imbue every employee with a clear vision and a sense of empowerment that helps them identify synergistic ideas and run with them” (“Build a Company Where Everyone’s Looking for New Ideas”, Harvard Business Review, page 1).

4) Architect for Innovation. As the velocity of change increases, businesses need IT organizations to be able to move more quickly. This requires an enterprise architecture built for agility. According to Jeanne Ross, the more agile companies have a high percentage of their core business processes digitized and they have as well standardized their technology architecture (Enterprise Architecture as Strategy, Jeanne Ross, page 12).

5) Look for disruptive innovations. I remember a professor of mine suggesting that we cannot predict the future when discussing futures research. But I believe that you can instead get closer to your customers than anyone else. CIOs should dedicate a non-trival portion of IT spend to germinating potentially disruptive ideas. They should use their design partners to select what gets early stage funding. Everyone here should act like a seed stage venture capitalist. You need to let people experiment. At the same time, design partners should set reasonable goals and actively measure performance toward goals.

6) Use analytics. Look at business analytics for areas of that could use IT’s help. Open up discussions with design partners for areas needing capability improvement. This is a great place to start. Look as well for where there are gaps in business delivery that could be drive better performance from further or improved digitization/automation. And once an innovation is initiated, analytics should actively ensure the management of  the innovation’s delivery.

Final remarks

There is always more that you can do to innovate. The key thing is to get innovation front and center on the IT agenda. Actively sponsor it and most importantly empower the team to do remarkable things. And when this happens, reward the teams that made it happen.

IT Is All About Data!
The Secret To Being A Successful CIO
Driving IT Business Alignment: One CIOs Journey
How Is The CIO Role Starting To Change?
The CIO Challenged

Twitter: @MylesSuer

Share
Posted in CIO, Data Governance | Tagged , , , , , | Leave a comment

Are The Banks Going to Make Retailers Pay for Their Poor Governance?

 

Retail and Data Governance

Retail and Data Governance

A couple months ago, I reached out to a set of CIOs on the importance of good governance and security. All of them agreed that both were incredibly important. However, one CIO retorted a very pointed remark by saying that “the IT leadership at these breached companies wasn’t stupid.” He continued by saying that when selling the rest of the C-Suite, the discussion needs to be about business outcomes and business benefits.  For this reason, he said that CIOs have struggled at selling the value of investments in governance and security investment. Now I have suggested previously that security pays because of the impact on “brand promise”.  And, I still believe this.

However, this week the ante was raised even higher. A district judge ruled that a group of banks can proceed to sue a retailer for negligence in their data governance and security. The decision could clearly lead to significant changes in the way the cost of fraud is distributed among parties within the credit card ecosystem. Where once banks and merchant acquirers would have shouldered the burden of fraud, this decision paves the way for more card-issuing banks to sue merchants for not adequately protecting their POS systems.

Accidents waste priceless time

Accidents waste priceless time

The judge’s ruling said that “although the third-party hackers’ activities caused harm, merchant played a key role in allowing the harm to occur.” The judge also determined that the bank suit against merchants was valid because the plaintiffs adequately showed that the retailer failed “to disclose that its data security systems were deficient.” This is interesting because it says that security systems should be sufficient and if not, retailers need to inform potentially affected stakeholders of their deficient systems. And while taking this step could avoid a lawsuit, it would likely increase the cost of interchange for more risky merchants. This would effectively create a risk premium for retailers that do not adequately govern and protect their IT environments.

There are broad implications for all companies who end up harming customer, partners, or other stakeholders by not keeping their security systems up to snuff. The question is, will this make good governance have enough of a business outcome and benefit that businesses will actually want to pay it forward — i.e. invest in good governance and security? What do you think? I would love to hear from you.

Related links

Solutions:

Enterprise Level Data Security

Hacking: How Ready Is Your Enterprise?

Gambling With Your Customer’s Financial Data

The State of Data Centric Security

Twitter: @MylesSuer

 

Share
Posted in Banking & Capital Markets, CIO, Data Governance, Data Security, Financial Services | Tagged , , , | Leave a comment

Relating the IoT to Enterprise Business Strategy

Business StrategyRecently, I got to speak to a CIO at a Global 500 Company about the challenges of running his IT organization. He said that one of his biggest challenges is getting business leaders to understand technology better. “I want my business leaders to be asking for digital services that support and build upon their product and service offerings”. I think that his perspective provides real insight to how businesses should be thinking about the so called Internet of Things (IoT), but let me get you there first.

What is the IoT?

According to Frank Burkitt of @Strategy, by 2029, an estimated 50 billion devices around the globe will be connected to the Internet. Perhaps a third will be computers, smartphones, tablets, and TVs. The remaining two-thirds will be “things”–sensors, actuators, and intelligent devices that monitor, control, analyze, and optimize our world. Frank goes on to say if your company wants to stake a claim in the IoT, you first need to develop a distinctive “way to play”—a clear value proposition that you can offer customers. This should be consistent with your enterprise’s overall capabilities system: the things you do best when you go to market.

While what Frank suggests make great sense, they do not in my opinion provide the strategic underpinning that business leaders need to link the IoT to their business strategy. Last week an article in Harvard Business Review by Michael Porter and James E. Heppelmann shared what business leaders need to do to apply the IoT to their businesses. According to Porter and Hepplemann, historical, enterprises have defined their businesses by the physical attributes of the products and services they produce. And while products have been mostly composed of mechanical and electrical parts, they are increasingly becoming complex systems that combine hardware, sensors, data storage, microprocessors, software, and data connectivity.

The IoT is really about creating a system of systems

Business StrategyPorter and Hepplemann share in their article how connectivity allows companies to evolve from making point solutions, to making more complex, higher-value “systems of systems”. According to Russell Ackoff, a system’s orientation views customer problems “as a whole and not on their parts taken separate” (Ackoff’s Best, Russell Ackoff, John Wiley and Sons, page 47). This change means that market winners will tend to view business opportunities from a larger versus a smaller perspective. It reminds me a lot of what Xerox did when it transformed itself from commoditized copiers to high priced software based document management where the printer represent an input device to a larger system. Porter and Hepplemann’s give the example of a company that sells tractors. Once a tractor is smart and connected, it becomes part of a highly interconnected agricultural management solution.

According to Porter and Hepplemann, the key element of “smart, connected products” is they take advantage of ubiquitous wireless connectivity to unleash an era where competition is increasingly about the size of the business problem solved. Porter and Hepplemann claim that as smart, connected products take hold, the idea of industries being defined by physical products or services alone will cease to have meaning. What sense does it make to talk about a “tractor industry” when tractors represent just a piece of an integrated system of products, services, software, and data designed to help farmers increase their crop yield?

Porter and Hepplemann claim, therefore, the phrase “Internet of Things” is not very helpful in understanding the phenomenon or even its implications. They say after all what makes smart, connected products fundamentally different is not the Internet, it is a redefinition of what is a product and the capabilities smart, connected products provide and the data they generate. Companies, therefore, need to look at how the IoT will transform the competition within their specific industries.

Like a business slogan, the IoT is about putting IT inside

Business StrategyIT leaders have a role to play in the IoT. They need to move IT from just assisting business management drive improvements to the company value chain to organizations  that as well embed IT in what become system oriented products. How perceptive, therefore, was my CIO friend.

Porter and Hepplemann claim connectivity serves two purposes. First, it allows information to be exchanged between a product and its operating environment, its maker, its users, and other products and systems. Second, connectivity enables some functions of the product to exist outside the physical device. Porter and Hepplemann give the example of Schindler’s PORT Technology that reduces elevator wait times by as much as 50% by predicting elevator demand patterns, calculating the fastest time to destination, and assigning the appropriate elevator to move passengers quickly. Porter and Hepplemann see as well intelligence and connectivity enabling an entirely new set of product functions and capabilities, which can be grouped into four categories: monitor, control, optimize, and autonomy. To be clear, a systems product can potentially incorporate all four.

  • Monitored products alert users to changes in circumstances or performance. They can provide a product’s operating characteristics and history. A company must choose the set customer value and define its competitive positioning. This has implications design, marketing, service, and warranty.
  • Controlled products can receive remote commands or have algorithms that are built into the device or reside in the product’s cloud. For example, “if pressure gets too high, shut off the valve” or “when traffic in a parking garage reaches a certain level, turn the overhead lighting on or off”.
  • Optimized products apply algorithms and analytics to in-use or historical data to improve output, utilization, and efficiency. Real-time monitoring data on product condition and product control capability enables firms to optimize service.
  • Autonomous product like are able to learn about their environment, self-diagnose their own service needs, and adapt to users’ preferences.

Smart, connected products expand opportunities for product differentiation

Geoffrey MooreIn a world where Geoffrey Moore sees differentiated products constantly being commoditized; smart, connected products dramatically expand opportunities for product differentiation and move the competition away from price alone. Knowing how customers actually use your products enhances a company’s ability to segment customers, customize products, set prices to better capture value, and extend value-added services. Smart, connected products, at the same time, create opportunities to broaden the value proposition beyond products per se, to include valuable data and enhanced service offerings. Broadening product definitions can raise barriers to entrants even higher. The powerful capabilities of smart, connected products not only reshape competition within an industry, but they can expand the very definition of the industry itself. For example, integrating smart, connected farm equipment—such as tractors, tillers, and planters—can enable better overall equipment performance.

Smart, connected products will not only reshape competition within an industry, but they can expand the very definition of the industry itself. Here Porter and Hepplemann are talking here about the competitive boundaries of an industry widen to encompass a set of related products that together meet a broader underlying need. The function of one product is optimized with other related products.

Porter and Hepplemann believe that smart, connected products allow as well companies to form new kinds of relationships with their customers. In many cases, this may require market participants to develop new marketing practices and skill sets. As companies accumulate and analyze product usage data, they will as well gain new insights into how products create value for customers, allowing better positioning of offerings and more effective communication of product value to customers. Using data analytics tools, firms will be able segment their markets in more-sophisticated ways, tailor product and service bundles that deliver greater value to each segment, and price those bundles to capture more of that value.

Some parting thoughts

So summarizing their position, Porter and Hepplemann believe the IoT is really about taking smart things and building solutions that solve bigger problems because one can architect the piece parts into a solution of solutions. This will impact marketplace dynamics and create competitive differentiators in a world of increasing product commodization. For me this is a roadmap forward especially for those at the later stages of product lifecycle curve.

Related links

Related Blogs

Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”

Solution Brief: The Intelligent Data Platform

Author Twitter: @MylesSuer

Share
Posted in Big Data | Tagged , , , , , | Leave a comment