Tag Archives: data security

Data First: Five Tips To Reduce the Risk of A Breach

Reduce the Risk of A Breach

Reduce the Risk of A Breach

This article was originally published on www.federaltimes.com

November – that time of the year. This year, November 1 was the start of Election Day weekend and the associated endless barrage of political ads. It also marked the end of Daylight Savings Time. But, perhaps more prominently, it marked the beginning of the holiday shopping season. Winter holiday decorations erupted in stores even before Halloween decorations were taken down. There were commercials and ads, free shipping on this, sales on that, singing, and even the first appearance of Santa Claus.

However, it’s not all joy and jingle bells. The kickoff to this holiday shopping season may also remind many of the countless credit card breaches at retailers that plagued last year’s shopping season and beyond. The breaches at Target, where almost 100 million credit cards were compromised, Neiman Marcus, Home Depot and Michael’s exemplify the urgent need for retailers to aggressively protect customer information.

In addition to the holiday shopping season, November also marks the next round of open enrollment for the ACA healthcare exchanges. Therefore, to avoid falling victim to the next data breach, government organizations as much as retailers, need to have data security top of mind.

According to the New York Times (Sept. 4, 2014), “for months, cyber security professionals have been warning that the healthcare site was a ripe target for hackers eager to gain access to personal data that could be sold on the black market. A week before federal officials discovered the breach at HealthCare.gov, a hospital operator in Tennessee said that Chinese hackers had stolen personal data for 4.5 million patients.”

Acknowledging the inevitability of further attacks, companies and organizations are taking action. For example, the National Retail Federation created the NRF IT Council, which is made up of 130 technology-security experts focused on safeguarding personal and company data.

Is government doing enough to protect personal, financial and health data in light of these increasing and persistent threats? The quick answer: no. The federal government as a whole is not meeting the data privacy and security challenge. Reports of cyber attacks and breaches are becoming commonplace, and warnings of new privacy concerns in many federal agencies and programs are being discussed in Congress, Inspector General reports and the media. According to a recent Government Accountability Office report, 18 out of 24 major federal agencies in the United States reported inadequate information security controls. Further, FISMA and HIPAA are falling short and antiquated security protocols, such as encryption, are also not keeping up with the sophistication of attacks. Government must follow the lead of industry and look for new and advanced data protection technologies, such as dynamic data masking and continuous data monitoring to prevent and thwart potential attacks.

These five principles can be implemented by any agency to curb the likelihood of a breach:

1. Expand the appointment and authority of CSOs and CISOs at the agency level.

2. Centralize the agency’s data privacy policy definition and implement on an enterprise level.

3. Protect all environments from development to production, including backups and archives.

4. Data and application security must be prioritized at the same level as network and perimeter security.

5. Data security should follow data through downstream systems and reporting.

So, as the season of voting, rollbacks, on-line shopping events, free shipping, Black Friday, Cyber Monday and healthcare enrollment begins, so does the time for protecting personal identifiable information, financial information, credit cards and health information. Individuals, retailers, industry and government need to think about data first and stay vigilant and focused.

This article was originally published on www.federaltimes.com. Please view the original listing here

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BCBS 239 – What Are Banks Talking About?

I recently participated on an EDM Council panel on BCBS 239 earlier this month in London and New York. The panel consisted of Chief Risk Officers, Chief Data Officers, and information management experts from the financial industry. BCBS 239 set out 14 key principles requiring banks aggregate their risk data to allow banking regulators to avoid another 2008 crisis, with a deadline of Jan 1, 2016.  Earlier this year, the Basel Committee on Banking Supervision released the findings from a self-assessment from the Globally Systemically Important Banks (GISB’s) in their readiness to 11 out of the 14 principles related to BCBS 239. 

Given all of the investments made by the banking industry to improve data management and governance practices to improve ongoing risk measurement and management, I was expecting to hear signs of significant process. Unfortunately, there is still much work to be done to satisfy BCBS 239 as evidenced from my findings. Here is what we discussed in London and New York.

  • It was clear that the “Data Agenda” has shifted quite considerably from IT to the Business as evidenced by the number of risk, compliance, and data governance executives in the room.  Though it’s a good sign that business is taking more ownership of data requirements, there was limited discussions on the importance of having capable data management technology, infrastructure, and architecture to support a successful data governance practice. Specifically capable data integration, data quality and validation, master and reference data management, metadata to support data lineage and transparency, and business glossary and data ontology solutions to govern the terms and definitions of required data across the enterprise.
  • With regard to accessing, aggregating, and streamlining the delivery of risk data from disparate systems across the enterprise and simplifying the complexity that exists today from point to point integrations accessing the same data from the same systems over and over again creating points of failure and increasing the maintenance costs of supporting the current state.  The idea of replacing those point to point integrations via a centralized, scalable, and flexible data hub approach was clearly recognized as a need however, difficult to envision given the enormous work to modernize the current state.
  • Data accuracy and integrity continues to be a concern to generate accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements. Many in the room acknowledged heavy reliance on manual methods implemented over the years and investing in Automating data integration and onboarding risk data from disparate systems across the enterprise is important as part of Principle 3 however, much of what’s in place today was built as one off projects against the same systems accessing the same data delivering it to hundreds if not thousands of downstream applications in an inconsistent and costly way.
  • Data transparency and auditability was a popular conversation point in the room as the need to provide comprehensive data lineage reports to help explain how data is captured, from where, how it’s transformed, and used remains a concern despite advancements in technical metadata solutions that are not integrated with their existing risk management data infrastructure
  • Lastly, big concerns regarding the ability to capture and aggregate all material risk data across the banking group to deliver data by business line, legal entity, asset type, industry, region and other groupings, to support identifying and reporting risk exposures, concentrations and emerging risks.  This master and reference data challenge unfortunately cannot be solved by external data utility providers due to the fact the banks have legal entity, client, counterparty, and securities instrument data residing in existing systems that require the ability to cross reference any external identifier for consistent reporting and risk measurement.

To sum it up, most banks admit they have a lot of work to do. Specifically, they must work to address gaps across their data governance and technology infrastructure.BCBS 239 is the latest and biggest data challenge facing the banking industry and not just for the GSIB’s but also for the next level down as mid-size firms will also be required to provide similar transparency to regional regulators who are adopting BCBS 239 as a framework for their local markets.  BCBS 239 is not just a deadline but the principles set forth are a key requirement for banks to ensure they have the right data to manage risk and ensure transparency to industry regulators to monitor system risk across the global markets. How ready are you?

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Which Method of Controls Should You Use to Protect Sensitive Data in Databases and Enterprise Applications? Part II

Sensitive Data

Protecting Sensitive Data

To determine what is the appropriate sensitive data protection method to use, you should first answer the following questions regarding the requirements:

  • Do you need to protect data at rest (in storage), during transmission, and/or when accessed?
  • Do some privileged users still need the ability to view the original sensitive data or does sensitive data need to be obfuscated at all levels?
  • What is the granularity of controls that you need?
    • Datafile level
    • Table level
    • Row level
    • Field / column level
    • Cell level
    • Do you need to be able to control viewing vs. modification of sensitive data?
    • Do you need to maintain the original characteristics / format of the data (e.g. for testing, demo, development purposes)?
    • Is response time latency / performance of high importance for the application?  This can be the case for mission critical production applications that need to maintain response times in the order of seconds or sub-seconds.

In order to help you determine which method of control is appropriate for your requirements, the following table provides a comparison of the different methods and their characteristics.

data

A combination of protection method may be appropriate based on your requirements.  For example, to protect data in non-production environments, you may want to use persistent data masking to ensure that no one has access to the original production data, since they don’t need to.  This is especially true if your development and testing is outsourced to third parties.  In addition, persistent data masking allows you to maintain the original characteristics of the data to ensure test data quality.

In production environments, you may want to use a combination of encryption and dynamic data masking.  This is the case if you would like to ensure that all data at rest is protected against unauthorized users, yet you need to protect sensitive fields only for certain sets of authorized or privileged users, but the rest of your users should be able to view the data in the clear.

The best method or combination of methods will depend on each scenario and set of requirements for your environment and organization.  As with any technology and solution, there is no one size fits all.

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Which Method of Controls Should You Use to Protect Sensitive Data in Databases and Enterprise Applications? Part I

Sensitive Data

Protecting Sensitive Data

I’m often asked to share my thoughts about protecting sensitive data. The questions that typically come up include:

  • Which types of data should be protected?
  • Which data should be classified as “sensitive?”
  • Where is this sensitive data located?
  • Which groups of users should have access to this data?

Because these questions come up frequently, it seems ideal to share a few guidelines on this topic.

When protecting the confidentiality and integrity of data, the first level of defense is Authentication and access control. However, data with higher levels of sensitivity or confidentiality may require additional levels of protection, beyond regular authentication and authorization methods.

There are a number of control methods for securing sensitive data available in the market today, including:

  • Encryption
  • Persistent (Static) Data Masking
  • Dynamic Data Masking
  • Tokenization
  • Retention management and purging

Encryption is a cryptographic method of encoding data.  There are generally, two methods of encryption:  symmetric (using single secret key) and asymmetric (using public and private keys).  Although there are methods of deciphering encrypted information without possessing the key, a good encryption algorithm makes it very difficult to decode the encrypted data without knowledge of the key.  Key management is usually a key concern with this method of control.  Encryption is ideal for mass protection of data (e.g. an entire data file, table, partition, etc.) against unauthorized users.

Persistent or static data masking obfuscates data at rest in storage.  There is usually no way to retrieve the original data – the data is permanently masked.  There are multiple techniques for masking data, including: shuffling, substitution, aging, encryption, domain-specific masking (e.g. email address, IP address, credit card, etc.), dictionary lookup, randomization, etc.  Depending on the technique, there may be ways to perform reverse masking  - this should be used sparingly.  Persistent masking is ideal for cases where all users should not see the original sensitive data (e.g. for test / development environments) and field level data protection is required.

Dynamic data masking de-identifies data when it is accessed.  The original data is still stored in the database.  Dynamic data masking (DDM) acts as a proxy between the application and database and rewrites the user / application request against the database depending on whether the user has the privilege to view the data or not.  If the requested data is not sensitive or the user is a privileged user who has the permission to access the sensitive data, then the DDM proxy passes the request to the database without modification, and the result set is returned to the user in the clear.  If the data is sensitive and the user does not have the privilege to view the data, then the DDM proxy rewrites the request to include a masking function and passes the request to the database to execute.  The result is returned to the user with the sensitive data masked.  Dynamic data masking is ideal for protecting sensitive fields in production systems where application changes are difficult or disruptive to implement and performance / response time is of high importance.

Tokenization substitutes a sensitive data element with a non-sensitive data element or token.  The first generation tokenization system requires a token server and a database to store the original sensitive data.  The mapping from the clear text to the token makes it very difficult to reverse the token back to the original data without the token system.  The existence of a token server and database storing the original sensitive data renders the token server and mapping database as a potential point of security vulnerability, bottleneck for scalability, and single point of failure. Next generation tokenization systems have addressed these weaknesses.  However, tokenization does require changes to the application layer to tokenize and detokenize when the sensitive data is accessed.  Tokenization can be used in production systems to protect sensitive data at rest in the database store, when changes to the application layer can be made relatively easily to perform the tokenization / detokenization operations.

Retention management and purging is more of a data management method to ensure that data is retained only as long as necessary.  The best method of reducing data privacy risk is to eliminate the sensitive data.  Therefore, appropriate retention, archiving, and purging policies should be applied to reduce the privacy and legal risks of holding on to sensitive data for too long.  Retention management and purging is a data management best practices that should always be put to use.

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The Pros and Cons: Data Integration from the Bottom-Up and the Top-Down

Data Integration from the Bottom-Up and the Top-Down

Data Integration from the Bottom-Up and the Top-Down

What are the first steps of a data integration project?  Most are at a loss.  There are several ways to approach data integration, and your approach depends largely upon the size and complexity of your problem domain.

With that said, the basic approaches to consider are from the top-down, or the bottom-up.  You can be successful with either approach.  However, there are certain efficiencies you’ll gain with a specific choice, and it could significantly reduce the risk and cost.  Let’s explore the pros and cons of each approach.

Top-Down

Approaching data integration from the top-down means moving from the high level integration flows, down to the data semantics.  Thus, you an approach, perhaps even a tool-set (using requirements), and then define the flows that are decomposed down to the raw data.

The advantages of this approach include:

The ability to spend time defining the higher levels of abstraction without being limited by the underlying integration details.  This typically means that those charged with designing the integration flows are more concerned with how they have to deal with the underlying source and target, and this approach means that they don’t have to deal with that issue until later, as they break down the flows.

The disadvantages of this approach include:

The data integration architect does not consider the specific needs of the source or target systems, in many instances, and thus some rework around the higher level flows may have to occur later.  That causes inefficiencies, and could add risk and cost to the final design and implementation.

Bottom-Up

For the most part, this is the approach that most choose for data integration.  Indeed, I use this approach about 75 percent of the time.  The process is to start from the native data in the sources and targets, and work your way up to the integration flows.  This typically means that those charged with designing the integration flows are more concerned with the underlying data semantic mediation than the flows.

The advantages of this approach include:

It’s typically a more natural and traditional way of approaching data integration.  Called “data-driven” integration design in many circles, this initially deals with the details, so by the time you get up to the integration flows there are few surprises, and there’s not much rework to be done.  It’s a bit less risky and less expensive, in most cases.

The disadvantages of this approach include:

Starting with the details means that you could get so involved in the details that you miss the larger picture, and the end state of your architecture appears to be poorly planned, when all is said and done.  Of course, that depends on the types of data integration problems you’re looking to solve.

No matter which approach you leverage, with some planning and some strategic thinking, you’ll be fine.  However, there are different paths to the same destination, and some paths are longer and less efficient than others.  As you pick an approach, learn as you go, and adjust as needed.

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Hacking: How Ready Is Your Enterprise?

 
Recent corporate data security challenges require companies to ask hard questions about enterprise readiness:

1)      How do you know if your firm is next in line?
2)      How well will your Information Technology team respond to an attempted breach?

Is your firm ready?

HackingOver the last year, a number of high profile data security breaches have taken place at major US corporations. However, as a business person, how do you know the answers to the above questions.  Do you know what is at risk? And as well with big data gathering so much attention these days, isn’t it kind of like putting all the eggs into one basket? According to the management scholar, Theodore Levitt, part of being a manager is the ability to ask questions. My goal today is to arm business managers with the questions to ask so they can determine the answers to both of the above questions.

Is your Big Data secure?

HackingBig Data is all the buzz today. How safe are your Big Data spaces? Do you know what is going into each of them? Judith Hurwitz, the President and CEO of Hurwitz & Associates, says that she worries about big data security. Judith even suggests that big data “introduces security risks into the company, unintended consequences can endanger the company”. According to Judith, these risks come in two forms:

1)      Big data sources can contain viruses as well as other forms of business risk
2)      Big data lakes if unprotected represent a major business risk from hacking

Clearly, protecting your big data comprehensively requires diligence, including data encryption. But just remember, big data may seem like a science project in the back room, but it puts in one place a significant volume of data that could damage your enterprise if exposed to the outside world.

Do you need better tools or better business processes?

SecurityWhile many of the discussions about recent hacks have focused on the importance of having the right and up to date tools in place, it is just as important to have the right business processes in place if you want to minimize the possibility of a breach and minimizes losses when a breach occurs.

From an accessibility and security prospective, security processes look at the extent to which access to information is restricted appropriately to authorized parties. Next, from an information management perspective, they should consider the entire information life cycle. Information should be protected during all phases of its life cycle. Security should start at the information planning phase, and for many, this implies different protection mechanisms for storing, sharing, and disposition of information.

To determine what questions a business person should be asking their security professionals, I went to COBIT 5. For those who do not know, COBIT is the standard your auditors use to evaluate your company’s technology per Sarbanes Oxley. Understanding what it recommends matters because CFOs that we have talked to say that after the recent hacks they believe they are about to get increased scrutiny from their auditors. If you want to understand what auditors will look for, you should study COBIT 5. COBIT 5 has even linked its security policy guidance to what your IT security management team should be running against—one more term, ISO/IEC 27000 standard. Want to impress your security management professionals? Ask them whether they are in compliance with ISO/IEC 27000.

Good information security requires policies and procedures

Now, let’s explore what COBIT 5 recommends for information governance and security. The first thing it recommends is that good information security requires policies and procedures are created and put in place. This sounds pretty reasonable. However, COBIT next insists—something that we all know is true as managers– enterprise culture and ethics are critical to making “security policies and procedures effective”.

What metrics then should business people use to judge whether their firm is managing information security appropriately. COBIT 5 suggest that you look for two things right off the top.

1)      How recently did your IT organization conduct a risk assessment for the services that it provides?
2)      Does your IT organization have a current security plan which is accepted and communication throughout the enterprise?

For the first, it is important that you then ask what percentage of IT services and programs are covered by a risk assessment and what percentage of security incidents taking place were not identified in the risk assessment. The first question tells you how actively your IT is managing security and the second tells you whether there a gaps and risks. Your goal here should be to ensure that “IT-related enterprise risk does not exceed your risk appetite and your risk tolerance”.

With regards to the security plan, you should be asking your IT leadership (your CIO or CISO) about the number of key security roles that have been clearly defined and about the number of security related incidents over time. As important, find out how many security solutions currently deviate from plan?  A timely review of these could clearly impact your probability of getting your systems hacked.

As a manager, you know that teams need policies and procedures to limit errors from happening and to manage them when they occur. So ask what are the procedures for managing through a security event? As important, ask about the percentage of services are confirmed to have alignment with the security plan. At the same time, you want to know about the number of security incidents caused by non-adherence to the security plan. For the future, you want to make sure as well that all new solutions being developed have from launch confirmed their alignment to the security plan.

Other critical things to consider include the number of security incidents that have caused financial loss, business disruption, and public embarrassment. This of course is a big one that should be small in number. Then ask about the number of IT services with outstanding security requirements? Next, what is the time required to grant, change, and remove access privileges and the frequency of security assessment against the latest standards and guidelines.

Concluding Remarks

Security is one area that you really need IT-Business Alignment. It is important, as a business professional, that you do your best to ensure that IT builds policies and procedures that conform to your corporate risk appetite. As well you need to assure that the governance, policies, and procedures for your IT organization run against are kept current and update. This includes ensuring that the data is governed from end to end in the IT environment.

Related links

Solutions: Enterprise Level Data Security
The State of Data Centric Security
Gambling With Your Customer’s Financial Data
Twitter: @MylesSuer

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Gambling With Your Customer’s Financial Data

CIOs and CFOs both dig data security

Financial dataIn my discussions with CIOs over the last couple of months, I asked them about the importance of a series of topics. All of them placed data security at the top of their IT priority list. Even their CFO counterparts, with whom they do not always see eye to eye, said they were very concerned about the business risk for corporate data. These CFOs said that they touch, as a part of owning business risk, security — especially from hacking. One CFO said that he worried, as well, about the impact of data security for compliance issues, including HIPAA and SOX. Another said this: “The security of data is becoming more and more important. The auditors are going after this. CFOs, for this reason, are really worried about getting hacked. This is a whole new direction, but some of the highly publicized recent hacks have scared a lot of folks and they combined represent to many of us a watershed event.”

Editor of CFO Magazine

According to David W. Owens the editor of CFO Magazine, even if you are using “secure” storage, such as internal drives and private clouds, the access to these areas can be anything but secure. Practically any employee can be carrying around sensitive financial and performance data in his or her pocket, at any time.” Obviously, new forms of data access have created new forms of data risk.

Are some retailers really leaving the keys in the ignition?

If I only hadGiven the like mind set from CIOs and CFOs, I was shocked to learn that some of the recently hacked retailers had been using outdated security software, which may have given hackers easier access company payment data systems. Most amazingly, some retailers had not even encrypted their customer payment data. Because of this, hackers were able to hide on the network for months and steal payment data, as customers continued to use their credit cards at the company’s point of sale locations.

Why weren’t these transactions encrypted or masked? In my 1998 financial information start-up, we encrypted our databases to protect against hacks of our customers’ personal financial data. One answer came from a discussion with a Fortune 100 Insurance CIO. This CIO said “CIO’s/CTO’s/CISO’s struggle with selling the value of these investment because the C Suite is only interested in hearing about investments with a direct impact on business outcomes and benefits”.

Enterprise security drives enterprise brand today

Brand ValueSo how should leaders better argue the business case for security investments? I want to suggest that the value of IT is its “brand promise”. For retailers, in particular, if a past purchase decision creates a perceived personal data security risk, IT becomes a liability to their corporations brand equity and potentially creates a negative impact on future sales. Increasingly how these factors are managed either supports or not the value of a company’s brand.

My message is this: Spend whatever it takes to protect your brand equity; Otherwise a security issue will become a revenue issue.

In sum, this means organizations that want to differentiate themselves and avoid becoming a brand liability need to further invest in their data centric security strategy and of course, encryption. The game is no longer just about securing particular applications. IT organizations need to take a data centric approach to securing customer data and other types of enterprise data. Enterprise level data governance rules needs to be a requirement. A data centric approach can mitigate business risk by helping organizations to understand where sensitive data is and to protect it in motion and at rest. 

Related links

Solutions: Enterprise Level Data Security
The State of Data Centric Security
How Is The CIO Role Starting To Change?
The CFO viewpoint on data
CFOs discuss their technology priorities
Twitter: @MylesSuer

 

 

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CFOs Discuss Their Technology Priorities

Recently, I had the opportunity to talk to a number of CFOs about their technology priorities. These discussions represent an opportunity for CIOs to hear what their most critical stakeholder considers important. The CFOs did not hesitate or need to think much about this question. They said three things make their priority list. They are better financial system reliability, better application integration, and better data security and governance. The top two match well with a recent KPMG study which found the biggest improvement finance executives want to see—cited by 91% of survey respondents—is in the quality of financial and performance insight obtained from the data they produce, followed closely by the finance and accounting organization’s ability to proactively analyze that information before it is stale or out of date”

TrustBetter financial system reliability

CFOs want to know that their systems work and are reliable. They want the data collected from their systems to be analyzed in a timely fashion. Importantly, CFOs say they are worried not only about the timeliness of accounting and financial data. This is because they increasingly need to manage upward with information.  For this reason, they want timely, accurate information produced for financial and business decision makers. Their goal is to drive out better enterprise decision making.

In manufacturing, for example, CFOs say they want data to span from the manufacturing systems to the distribution system. They want to be able to push a button and get a report. These CFOs complain today about the need to manually massage and integrate data from system after system before they get what they and their business decision makers want and need.

IntegrationBetter Application Integration

CFOs really feel the pain of systems not talking to each other. CFOs know firsthand that they have “disparate systems” and that too much manual integration is going on. For them, they see firsthand the difficulties in connecting data from the frontend to backend systems. They personally feel the large number of manual steps required to pull data. They want their consolidation of account information to be less manual and to be more timely. One CFO said that “he wants the integration of the right systems to provide the right information to be done so they have the right information to manage and make decisions at the right time”.

Data Security and Governance

securityCFOs, at the same time, say they have become more worried about data security and governance. Even though CFOs believe that security is the job of the CIO and their CISO, they have an important role to play in data governance. CFOs say they are really worried about getting hacked. One CFO told me that he needs to know that systems are always working properly. Security of data matters today to CFOs for two reasons. First, data has a clear material impact. Just take a look at the out of pocket and revenue losses coming from the breach at Target. Second, CFOs, which were already being audited for technology and system compliance, feel that their audit firms will be obligated to extend what they were doing in security and governance and go as a part of regular compliance audits. One CFO put it this way. “This is a whole new direction for us. Target scared a lot of folks and will be to many respects a watershed event for CFOs”.

Take aways

So the message here is that CFOs prioritize three technology objectives for their CIOs– better IT reliability, better application integration, and improved data security and governance. Each of these represents an opportunity to make the CFOs life easier but more important to enable them to take on a more strategic role. The CFOs, that we talked to, want to become one of the top three decision makers in the enterprise. Fixing these things for CFOs will enable CIOs to build a closer CFO and business relationships.

Related links

Solution Brief: The Intelligent Data Platform

Solution Brief: Secure at Source

Related Blogs

The CFO Viewpoint upon Data

How CFOs can change the conversation with their CIO?

New type of CFO represents a potent CIO ally

Competing on Analytics

The Business Case for Better Data Connectivity

Twitter: @MylesSuer

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In a Data First World, IT must Empower Business Change!

IT must Empower Business ChangeYou probably know this already, but I’m going to say it anyway: It’s time you changed your infrastructure. I say this because most companies are still running infrastructure optimized for ERP, CRM and other transactional systems. That’s all well and good for running IT-intensive, back-office tasks. Unfortunately, this sort of infrastructure isn’t great for today’s business imperatives of mobility, cloud computing and Big Data analytics.

Virtually all of these imperatives are fueled by information gleaned from potentially dozens of sources to reveal our users’ and customers’ activities, relationships and likes. Forward-thinking companies are using such data to find new customers, retain existing ones and increase their market share. The trick lies in translating all this disparate data into useful meaning. And to do that, IT needs to move beyond focusing solely on transactions, and instead shine a light on the interactions that matter to their customers, their products and their business processes.

They need what we at Informatica call a “Data First” perspective. You can check out my first blog first about being Data First here.

A Data First POV changes everything from product development, to business processes, to how IT organizes itself and —most especially — the impact IT has on your company’s business. That’s because cloud computing, Big Data and mobile app development shift IT’s responsibilities away from running and administering equipment, onto aggregating, organizing and improving myriad data types pulled in from internal and external databases, online posts and public sources. And that shift makes IT a more-empowering force for business change. Think about it: The ability to connect and relate the dots across data from multiple sources finally gives you real power to improve entire business processes, departments and organizations.

I like to say that the role of IT is now “big I, little t,” with that lowercase “t” representing both technology and transactions. But that role requires a new set of priorities. They are:

  1. Think about information infrastructure first and application infrastructure second.
  2. Create great data by design. Architect for connectivity, cleanliness and security. Check out the eBook Data Integration for Dummies.
  3. Optimize for speed and ease of use – SaaS and mobile applications change often. Click here to try Informatica Cloud for free for 30 days.
  4. Make data a team sport. Get tools into your users’ hands so they can prepare and interact with it.

I never said this would be easy, and there’s no blueprint for how to go about doing it. Still, I recognize that a little guidance will be helpful. In a few weeks, Informatica’s CIO Eric Johnson and I will talk about how we at Informatica practice what we preach.

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Posted in B2B, B2B Data Exchange, Big Data, Business Impact / Benefits, Data Integration, Data Security, Data Services, Enterprise Data Management | Tagged , , , | Leave a comment

Malcolm Gladwell, Big Data and What’s to be Done About Too Much Information

Malcolm Gladwell wrote an article in The New Yorker magazine in January, 2007 entitled “Open Secrets.” In the article, he pointed out that a national-security expert had famously made a distinction between puzzles and mysteries.

Big Data Enterprise Data Management

Malcolm Gladwell has written about the perils of too much information-very relevant in the era of Big Data

Osama bin Laden’s whereabouts were, for many years, a puzzle. We couldn’t find him because we didn’t have enough information. The key to the puzzle, it was assumed, would eventually come from someone close to bin Laden, and until we could find that source, bin Laden would remain at large. In fact, that’s precisely what happened. Al-Qaida’s No. 3 leader, Khalid Sheikh Mohammed, gave authorities the nicknames of one of bin Laden’s couriers, who then became the linchpin to the CIA’s efforts to locate Bin Laden.

By contrast, the problem of what would happen in Iraq after the toppling of Saddam Hussein was a mystery. It wasn’t a question that had a simple, factual answer. Mysteries require judgments and the assessment of uncertainty, and the hard part is not that we have too little information but that we have too much.

This was written before “Big Data” was a household word and it begs the very interesting question of whether organizations and corporations that are, by anyone’s standards, totally deluged with data, are facing puzzles or mysteries. Consider the amount of data that a company like Western Union deals with.

Western Union is a 160-year old company. Having built scale in the money transfer business, the company is in the process of evolving its business model by enabling the expansion of digital products, growth of web and mobile channels, and a more personalized online customer experience. Sounds good – but get this: the company processes more than 29 transactions per seconds on average. That’s 242 million consumer-to-consumer transactions and 459 million business payments in a year. Nearly a billion transactions – a billion! As my six-year-old might say, that number is big enough “to go to the moon and back.” Layer on top of that the fact that the company operates in 200+ countries and territories, and conducts business in 120+ currencies. Senior Director and Head of Engineering Abhishek Banerjee has said, “The data is speaking to us. We just need to react to it.” That implies a puzzle, not a mystery – but only if data scientists are able to conduct statistical modeling and predictive analysis, systematically noting trends in sending and receiving behaviors. Check out what Banerjee and Western Union CTO Sanjay Saraf have to say about it here.

Or consider General Electric’s aggressive and pioneering move into what’s dubbed as the industrial internet. In a white paper entitled “The Case for an Industrial Big Data Platform: Laying the Groundwork for the New Industrial Age,” GE reveals some of the staggering statistics related to the industrial equipment that it manufactures and supports (services comprise 75% of GE’s bottom line):

  • A modern wind turbine contains approximately 50 sensors and control loops which collect data every 40 milliseconds.
  • A farm controller then receives more than 30 signals from each turbine at 160-millisecond intervals.
  • At every one-second interval, the farm monitoring software processes 200 raw sensor data points with various associated properties with each turbine.
big data

Jet engines and wind turbines generate enormous amounts of data

Phew! I’m no electricity operations expert, and you probably aren’t either. And most of us will get no further than simply wrapping our heads around the simple fact that GE turbines are collecting a LOT of data. But what the paper goes on to say should grab your attention in a big way: “The key to success for this wind farm lies in the ability to collect and deliver the right data, at the right velocity, and in the right quantities to a wide set of well-orchestrated analytics.” And the paper goes on to recommend that anyone involved in the Industrial Internet revolution strongly consider its talent requirements, with the suggestion that Chief Data officers and/or Data Scientists may be the next critical hires.

Which brings us back to Malcolm Gladwell. In the aforementioned article, Gladwell goes on to pull apart the Enron debacle, and argues that it was a prime example of the perils of too much information. “If you sat through the trial of (former CEO) Jeffrey Skilling, you’d think that the Enron scandal was a puzzle. The company, the prosecution said, conducted shady side deals that no one quite understood. Senior executives withheld critical information from investors…We were not told enough—the classic puzzle premise—was the central assumption of the Enron prosecution.” But in fact, that was not true. Enron employed complicated – but perfectly legal–accounting techniques used by companies that engage in complicated financial trading. Many journalists and professors have gone back and looked at the firm’s regulatory filings, and have come to the conclusion that, while complex and difficult to identify, all of the company’s shenanigans were right there in plain view. Enron cannot be blamed for covering up the existence of its side deals. It didn’t; it disclosed them. As Gladwell summarizes:

“Puzzles are ‘transmitter-dependent’; they turn on what we are told. Mysteries are ‘receiver dependent’; they turn on the skills of the listener.” 

I would argue that this extremely complex, fast moving and seismic shift that we call Big Data will favor those who have developed the ability to attune, to listen and make sense of the data. Winners in this new world will recognize what looks like an overwhelming and intractable mystery, and break that mystery down into small and manageable chunks and demystify the landscape, to uncover the important nuggets of truth and significance.

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