Tag Archives: Analytics

Analytics Stories: A Banking Case Study

Right to winAs I have shared within other post 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. In banking, the right to win increasingly comes from improving two core sets of business capabilities—risk management and customer service.

Significant change has occurred in risk management over the last few years following theAnalytics subprime crisis and the subsequent credit crunch. These environmental changes have put increased regulatory pressure upon banks around the world. Among other things, banks need to comply with measures aimed at limiting the overvaluation of real estate assets and at preventing money laundering. A key element of handling these is to ensuring that go forward business decisions are made consistently using the most accurate business data available. It seems clear that data consistency can determine the quality of business operations especially business risk.

At the same time as banks need to strengthen their business capabilities around operations, and in particular risk management, they also need to use better data to improve the loyalty of their existing customer base.

Banco Popular launches itself into the banking vanguard

Banco Popular is an early responder regarding the need for better banking data consistency. Its leadership created a Quality of Information Office (the Office uniquely is not based within IT but instead with the Office of the President) with the mandate of delivering on two business objectives:

  1. Ensuring compliance with governmental regulations occurs
  2. Improving customer satisfaction based on accurate and up-to-date information

Part of the second objective is aimed at ensuring that each of Banco Popular’s customers was offered the ideal products for their specific circumstances. This is interesting because by its nature it assists in obtainment of the first objective. To validate it achieves both mandates, the Office started by creating an “Information Quality Index”. The Index is created using many different types of data relating to each of the bank’s six million customers–including addresses, contact details, socioeconomic data, occupation data, and banking activity data. The index is expressed in percentage terms, which reflects the quality of the information collected for each individual customer. The overarching target set for the organization is a score of 90 percent—presently, the figure sits at 75 percent. There is room to grow and improve!

Current data management systems limit obtainment of its business goals

Unfortunately, the millions of records needed by the Quality Information Office are spread across different tables in the organization’s central computing system and must be combined into one information file for each customer to be useful to business users. The problem is that they had depended on third parties to manually pull and clean up this data. This approach with the above mandates proved too slow to be executed in timely fashion. This, in turn, has impacted the quality of their business capabilities for risk and customer service. According to Banco Popular, their approach did not create the index and other analyses “with the frequency that we wanted and examining the variables of interest to us,” explains Federico Solana, an analyst at the Banco Popular Quality of Information Office.

Creating the Quality Index was just too time consuming and costly. But not improving data delivery performance had a direct impact on decision making.

Automation proves key to better business processes

TrustTo speed up delivery of its Quality Index, Banco Popular determined it needed to automate it’s creation of great data—data which is trustworthy and timely. According to Tom Davenport, “you can’t be analytical without data and you can’t be really good at analytics without really good data”. (Analytics at Work, 2010, Harvard Business Press, Page 23). Banco Popular felt that automating the tasks of analyzing and comparing variables would increase the value of data at lower cost and ensuring a faster return on data.

In addition to fixing the Quality Index, Banco Popular needed to improve its business capabilities around risk and customer service automation. This aimed at improving the analysis of mortgages while reducing the cost of data, accelerating the return on data, and boosting business and IT productivity.

Everything, however, needed to start with the Quality Index. After the Quality Index was created for individuals, Banco Popular created a Quality of Information Index for Legal Entities and is planning to extend the return on data by creating indexes for Products and Activities. For the Quality Index related to legal entities, the bank included variables that aimed at preventing the consumption of capital as well as other variables used to calculate the probability of underpayments and Basel models. Variables are classified as essential, required, and desirable. This evaluation of data quality allows for the subsequent definition of new policies and initiatives for transactions, the network of branches, and internal processes, among other aspects. In addition, the bank is also working on the in-depth analysis of quality variables for improving its critical business processes including mortgages.

Some Parting Remarks

In the end, Banco Popular has shown the way forward for analytics. In banking the measures of performance are often known, however, what is problematic is ensuring the consistency of decision making across braches and locations. By working first on data quality, Banco Popular ensured that the quality of data measures are consistent and therefore, it can now focus its attentions on improving underling business effectiveness and efficiency.

Related links

Related Blogs

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

 

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Who Owns Enterprise Analytics and Data?

processing dataWith the increasing importance of enterprise analytics, the question becomes who should own the analytics and data agenda. This question really matters today because, according to Thomas Davenport, “business processes are among the last remaining points of differentiation.” For this reason, Davenport even suggests that businesses that create a sustainable right to win use analytics to “wring every last drop of value from their processes”.

The CFO is the logical choice?

enterpriseIn talking with CIOs about both enterprise analytics and data, they are clear that they do not want to become their company’s data steward. They insist instead that they want to be an enabler of the analytics and data function. So what business function then should own enterprise analytics and data? Last week an interesting answer came from a CFO Magazine Article by Frank Friedman. Frank contends that CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”.

To justify his position, Frank made the following claims:

  1. CFOs own most of the unprecedented quantities of data that businesses create from supply chains, product processes, and customer interactions
  2. Many CFOs already use analytics to address their organization’s strategic issues
  3. CFOs uniquely can act as a steward of value and an impartial guardian of truth across the organizations. This fact gives them the credibility and trust needed when analytics produce insights that effectively debunk currently accepted wisdom

Frank contends as well that owning the analytics agenda is a good thing because it allows CFOs to expand their strategic leadership role in doing the following:

  • Growing top line revenue
  • Strengthening their business ties
  • Expanding the CFO’s influence outside the finance function.

Frank suggests as well that analytics empowers the CFO to exercise more centralized control of operational business decision making. The question is what do other CFOs think about Frank’s position?

CFOs clearly have an opinion about enterprise analytics and data

A major Retail CFO says that finance needs to own “the facts for the organization”—the metrics and KPIs. And while he honestly admits that finance organizations in the past have not used data well, he claims finance departments need to make the time to become truly data centric. He said “I do not consider myself a data expert, but finance needs to own enterprise data and the integrity of this data”. This CFO claims as well that “finance needs to use data to make sure that resources are focused on the right things; decisions are based on facts; and metrics are simple and understandable”. A Food and Beverage CFO agrees with the Retail CFO by saying that almost every piece of data is financial in one way or another. CFOs need to manage all of this data since they own operational performance for the enterprise. CFOs should own the key performance indicators of the business.

CIOs should own data, data interconnect, and system selection

A Healthcare CFO said he wants, however, the CIO to own data systems, data interconnect, and system selection. However, he believes that the finance organization is the recipient of data. “CFOs have a major stake in data. CFOs need to dig into operational data to be able to relate operations to internal accounting and to analyze things like costs versus price”. He said that “the CFOs can’t function without good operational data”.

An Accounting Firm CFO agreed with the Healthcare CFO by saying that CIOs are a means to get data. She said that CFOs need to make sense out of data in their performance management role. CFOs, therefore, are big consumers of both business intelligence and analytics. An Insurance CFO concurred by saying CIOs should own how data is delivered.

CFOs should be data validators

Data AnalysisThe Insurance CFOs said, however, CFOs need to be validators of data and reports. They should, as a result, in his opinion be very knowledgeable on BI and Analytics. In other words, CFOs need to be the Underwriters Laboratory (UL) for corporate data.

Now it is your chance

So the question is what do you believe? Does the CFO own analytics, data, and data quality as a part of their operational performance role? Or is it a group of people within the organization? Please share your opinions below.

Related links

Solution Brief: The Intelligent Data Platform

Related Blogs

CFOs Move to Chief Profitability Officer
CFOs Discuss Their Technology Priorities
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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Posted in CIO, Data First, Data Governance, Enterprise Data Management | Tagged , , , | 6 Comments

Analytics Stories: A Financial Services Case Study

As I indicated in my last case study regarding competing on analytics, Thomas H. Davenport believes “business processes are among the last remaining points of differentiation.” For this reason, Davenport contends that businesses that create a sustainable right to win use analytics to “wring every last drop of value from their processes”. For financial services, the mission critical areas needing process improvement center are around improving the consistency of decision making and making the management of regulatory and compliance more efficient and effective.

Why does Fannie Mae need to compete on analytics?

Fannie MaeFannie Mae is in the business of enabling people to buy, refinance, or rent homes. As a part of this, Fannie Mae says it is all about keeping people in their homes and getting people into new homes. Foundational to this mission is the accurate collection and reporting of data for decision making and risk management. According to Tracy Stephan at Fannie Mae, their “business needs to have the data to make decisions in a more real time basis. Today, this is all about getting the right data to the right people at the right time”.

Fannie Mae claims when the mortgage crisis hit, a lot of the big banks stopped lending and this meant that Fannie Mae among others needed to pick up the slack. Their action here, however, caused the Federal Government to require them to report monthly and quarterly against goals that the Federal Government set for it. “This meant that there was not room for error in how data gets reported”. In the end, Fannie Mae says three business imperatives drove it’s need to improve its reporting and its business processes:

  1. To ensure that go forward business decisions were made consistently using the most accurate business data available
  2. To avoid penalties by adhering to Dodd-Frank and other regulatory requirements established for it after the 2008 Global Financial Crisis
  3. To comply with reporting to Federal Reserve and Wall Street regarding overall business risk as a function of: data quality and accuracy, credit-worthiness of loans, and risk levels of investment positions.

Delivering required Fannie Mae to change how it managed data

AnalyticsGiven these business imperatives, IT leadership quickly realized it needed to enable the business to use data to truly drive better business processes from end to end of the organization. However, this meant enabling Fannie Mae’s business operations teams to more effectively and efficiently manage data. This caused Fannie Mae to determine that it needed a single source of truth whether it was for mortgage applications or the passing of information securely to investors. This need required Fannie Mae to establish the ability to share the same data across every Fannie Mae repository.

But there was a problem. Fannie Mae needed clean and correct data collected and integrated from more than 100 data sources. Fannie Mae determined that doing so with its current data processes could not scale. And as well, it determined that its data processes would not allow it to meet its compliance reporting requirements. At the same time, Fannie Mae needed to deliver more proactive management of compliance. This required that it know how critical business data enters and flows through each of its systems. This includes how data was changed by multiple internal processing and reporting applications. As well, Fannie Mae leadership felt that this was critical to ensure traceability to the individual user.

The solution

analyticsPer its discussions with business customers, Fannie Mae’s IT leadership determined that it needed to get real time, trustworthy data to improve its business operations and to improve its business processes and decision making. As said, these requirements could not be met with its historical approaches to integrating and managing data.

Fannie Mae determined that it needed to create a platform that was high availability, scalable, and largely automating its management of data quality management.  At the same time, the platform needed to provide the ability to create a set of business glossaries with clear data lineages. Fannie Mae determined it needed effectively a single source of truth across all of its business systems. According to Tracy Stephan, IT Director, Fannie Mae, “Data quality is the key to the success of Fannie Mae’s mission of getting the right people into the right homes. Now all our systems look at the same data – that one source of truth – which gives us great comfort.” To learn more specifics about how Fannie Mae improved its business processes and demonstrated that it is truly “data driven”, please click on this video of their IT leadership.

Related links
Solution Brief: The Intelligent Data Platform
Related Blogs
Thomas Davenport Book “Competing On Analytics”
Competing on Analytics
The Business Case for Better Data Connectivity
The CFO Viewpoint upon Data
What an enlightened healthcare CEO should tell their CIO?

Twitter: @MylesSuer

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The Streetlight Is Watching You

The Streetlight Is Watching You

The Streetlight Is Watching You

We are hugely dependent upon technology and sometimes take it for granted. It is always worth reminding ourselves where it all began so we can fully appreciate how lucky we are. Take the Light Emitting Diodes (LEDs) for example. They have come a long way in a relatively short time. They were used first as low-intensity light emitters in electronic devices, it is difficult to believe anyone would foresee them one day lighting our homes.

The future of lighting may first be peeking through at Newark Liberty Airport in New Jersey. The airport has installed 171 new LED-based light fixtures that include a variety of sensors to detect and record what’s going in the airport, as reported by Diane Cardwell in The New York Times. Together they make a network of devices that communicates wirelessly and allows authorities to scan license plates of passing cars, watch out for lines and delays, and check out travelers for suspicious activities.

I get the feeling that Newark’s new gear will not be the last of lighting-based digital networks. Over the last few years, LED street lights have gone from something cities would love to have to the sector standard. That the market has shifted so swiftly is thanks to the efforts of early movers such as the City of Los Angeles, which last year completed the world’s largest LED street light replacement project, with LED fixtures installed on 150,000 streetlights.

Los Angeles is certainly not alone in making the switch to LED street lighting. In March 2013, Las Vegas outfitted 50,000 streetlights with LED fixtures. One month later, the Austin TX announced plans to install 35,000 LED street lights. Not to be outdone, New York City, is planning to go all-LED by 2017, which would save $14 million and many tons of carbon emissions each year.

The impending switch to LEDs is an excellent opportunity for LED light fixture makers and Big Data software vendors like Informatica. These fixtures are made with a wide variety of sensors that can be tailored to whatever the user wants to detect, including temperature, humidity, seismic activity, radiation, audio, and video, among other things. The sensors could even detect and triangulate the source of a gunshot.

This steady stream of real-time data collected from these fixtures can be transformed into torrents of small messages and events with unprecedented agility using Informatica Vibe Data Stream. Analyzed data can then be distributed to various governmental and non-governmental agencies, such as; law enforcement, environmental monitors, retailers, etc.

If I were to guess the number of streetlights in the world, I would say 4 billion. Upgrading these is a “once-in-a-generation opportunity” to harness “lots of data, i.e., Sensory big data.”

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Download the Informatica Big Data Edition Trial and Unleash the Power of Hadoop

Cloudera Hadoop

Big Data Edition Trial Sandbox for Cloudera

Come and get it.  For developers hungry to get their hands on Informatica on Hadoop, a downloadable free trial of Informatica Big Data Edition was launched today on the Informatica Marketplace.  See for yourself the power of the killer app on Hadoop from the leader in data integration and quality.

Thanks to the generous help of our partners, the Informatica Big Data team has preinstalled the Big Data Edition inside the sandbox VMs of the two leading Hadoop distributions.  This empowers Hadoop and Informatica developers to easily try the codeless, GUI driven Big Data Edition to build and execute ETL and data integration pipelines natively on Hadoop for Big Data analytics.

Informatica Big Data Edition is the most complete and powerful suite for Hadoop data pipelines and can increase productivity up to 5 times. Developers can leverage hundreds of out-of-the-box Informatica pre-built transforms and connectors for structured and unstructured data processing on Hadoop.  With the Informatica Vibe Virtual Data Machine running directly on each node of the Hadoop cluster, the Big Data Edition can profile, parse, transform and cleanse data at any scale to prepare data for data science, business intelligence and operational analytics.

The Informatica Big Data Edition Trial Sandbox VMs will have a 60 day trial version of the Big Data Edition preinstalled inside a 1-node Hadoop cluster.  The trials include sample data and mappings as well as getting started documentation and videos.  It is possible to try your own data with the trials, but processing is limited to the 1-node Hadoop cluster and the machine you have it running on.  Any mappings you develop in the trial can be easily moved on to a production Hadoop cluster running the Big Data Edition. The Informatica Big Data Edition also supports MapR and Pivotal Hadoop distributions, however, the trial is currently only available for Cloudera and Hortonworks.

Hadoop Hortonworks

Big Data Edition Trial Sandbox for Hortonworks

Accelerate your ability to bring Hadoop from the sandbox into production by leveraging Informatica’s Big Data Edition. Informatica’s visual development approach means that more than one hundred thousand existing Informatica developers are now Hadoop developers without having to learn Hadoop or new hand coding techniques and languages. Informatica can help organizations easily integrate Hadoop into their enterprise data infrastructure and bring the PowerCenter data pipeline mappings running on traditional servers onto Hadoop clusters with minimal modification. Informatica Big Data Edition reduces the risk of Hadoop projects and increases agility by enabling more of your organization to interact with the data in your Hadoop cluster.

To get the Informatica Big Data Edition Trial Sandbox VMs and more information please visit Informatica Marketplace

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Reflections Of A Former Data Analyst (Part 2) – Changing The Game For Data Plumbing

 

Elephant cleansing

Cleaning. Sometimes is challenging!

In my last blog I promised I would report back my experience on using Informatica Data Quality, a software tool that helps automate the hectic, tedious data plumbing task, a task that routinely consumes more than 80% of the analyst time. Today, I am happy to share what I’ve learned in the past couple of months.

But first, let me confess something. The reason it took me so long to get here was that I was dreaded by trying the software.  Never a savvy computer programmer, I was convinced that I would not be technical enough to master the tool and it would turn into a lengthy learning experience. The mental barrier dragged me down for a couple of months and I finally bit the bullet and got my hands on the software. I am happy to report that my fear  was truly unnecessary –  It took me one half day to get a good handle on most features in the Analyst Tool, a component  of the Data Quality designed for analyst and business users,   then I spent 3 days trying to figure out how to maneuver the Developer Tool, another key piece of the Data Quality offering mostly used by – you guessed it, developers and technical users.  I have to admit that I am no master of the Developer Tool after 3 days of wrestling with it, but, I got the basics and more importantly, my hands-on interaction with the entire software helped me understand the logic behind the overall design, and see for myself  how analyst and business user can easily collaborate with their IT counterpart within our Data Quality environment.

To break it all down, first comes to Profiling. As analyst we understand too well the importance of profiling as it provides an anatomy of the raw data we collected. In many cases, it is a must have first step in data preparation (especially when our  raw data came from different places and can also carry different formats).  A heavy user of Excel, I used to rely on all the tricks available in the spreadsheet to gain visibility of my data. I would filter, sort, build pivot table, make charts to learn what’s in my raw data.  Depending on how many columns in my data set, it could take hours, sometimes days just to figure out whether the data I received was any good at all, and how good it was.

which one do you like better?

which one do you like better?

Switching to the Analyst Tool in Data Quality, learning my raw data becomes a task of a few clicks – maximum 6 if I am picky about how I want it to be done.  Basically I load my data, click on a couple of options, and let the software do the rest.  A few seconds later I am able to visualize the statistics of the data fields I choose to examine,  I can also measure the quality of the raw data by using Scorecard feature in the software. No more fiddling with spreadsheet and staring at busy rows and columns.  Take a look at the above screenshots and let me know your preference?

Once I decide that my raw data is adequate enough to use after the profiling, I still need to clean up the nonsense in it before performing any analysis work, otherwise  bad things can happen — we call it garbage in garbage out. Again, to clean and standardize my data, Excel came to rescue in the past.  I would play with different functions and learn new ones, write macro or simply do it by hand. It was tedious but worked if I worked on static data set. Problem however, was when I needed to incorporate new data sources in a different format, many of the previously built formula would break loose and become inapplicable. I would have to start all over again. Spreadsheet tricks simply don’t scale in those situation.

Rule Builder in Analyst Tool

Rule Builder in Analyst Tool

With Data Quality Analyst Tool, I can use the Rule Builder to create a set of logical rules in hierarchical manner based on my objectives,  and test those rules to see the immediate results. The nice thing is, those rules are not subject to data format, location, or size, so I can reuse them when the new data comes in.  Profiling can be done at any time so I can re-examine my data after applying the rules, as many times as I like. Once I am satisfied with the rules, they will be passed on to my peers in IT so they can create executable rules based on the logic I create and run them automatically in production. No more worrying about the difference in format, volume or other discrepancies in the data sets, all the complexity is taken care of by the software, and all I need to do is to build meaningful rules to transform the data to the appropriate condition so I can have good quality data to work with for my analysis.  Best part? I can do all of the above without hassling my IT – feeling empowered is awesome!

Changing The Game For Data Plumbing

Use the Right Tool for the Job

Use the right tool for the right job will improve our results, save us time, and make our jobs much more enjoyable. For me, no more Excel for data cleansing after trying our Data Quality software, because now I can get a more done in less time, and I am no longer stressed out by the lengthy process.

I encourage my analyst friends to try Informatica Data Quality, or at least the Analyst Tool in it.  If you are like me, feeling weary about the steep learning curve then fear no more. Besides, if Data Quality can cut down your data cleansing time by half (mind you our customers have reported higher numbers), how many more predictive models you can build, how much you will learn, and how much faster you can build your reports in Tableau, with more confidence?

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The Data-Driven CMO: A Q&A with Glenn Gow (CEO of Crimson Research)

Q&A with Crimson Research

I recently had the opportunity to have a very interesting discussion with Glenn Gow, the CEO of Crimson Marketing.  I was impressed at what an interesting and smart guy he was, and with the tremendous insight he has into the marketing discipline.  He consults with over 150 CMOs every year, and has a pretty solid understanding about the pains they are facing, the opportunities in front of them, and the approaches that the best-of-the-best are taking that are leading them towards new levels of success.

I asked Glenn if he would be willing to do a Q&A in order to share some of his insight.  I hope you find his perspective as interesting as I did!

 crimson_logo

______________________________________________

Q: What do you believe is the single biggest advantage that marketers have today?

A: Being able to use data in marketing is absolutely your single biggest competitive advantage as a marketer.  And therefore your biggest challenge is capturing, leveraging and rationalizing that data.  The marketers we speak with tend to fall into two buckets.

  1. Those who understand that the way they manage data is critical to their marketing success.  These marketers use data to inform their decisions, and then rely on it to measure their effectiveness.
  2. Those who haven’t yet discovered that data is the key to their success. Often these people start with systems in mind – marketing automation, CRM, etc.  But after implementing and beginning to use these systems, they almost always come to the realization that they have a data problem.

______________________________________________

Q:  How has this world of unprecedented data sources and volumes changed the marketing discipline?

A:  In short… dramatically.  The shift has really happened in the last two years. The big impetus for this change has really been the availability of data.  You’ve probably heard this figure, but Google’s Eric Schmidt likes to say that every two days now, we create as much information as we did from the dawn of civilization until 2003.

We believe this is a massive opportunity for marketers.  The question is, how do we leverage this data.  How do we pull the golden nuggets out that will help us do our jobs better.  Marketers now have access to information they’ve never had access to or even contemplated before.  This gives them the ability to become a more effective marketer. And by the way… they have to!  Customers expect them to!

For example, ad re-targeting.  Customers expect to be shown ads that are relevant to them, and if marketers don’t successfully do this, they can actually damage their brand.

In addition, competitors are taking full advantage of data, and are getting better every day at winning the hearts and minds of their customers – so marketers need to act before their competitors do.

Marketers have a tremendous opportunity – rich data is available and the technology is available to harness it is now, so that they can win a war that they could never before.

______________________________________________

Q:  Where are the barriers they are up against in harnessing this data?

A:
  I’d say that barriers can really be broken down into 4 main buckets: existing architecture, skill sets, relationships, and governance.

  • Existing Architecture: The way that data has historically been collected and stored doesn’t have the CMO’s needs in mind.  The CMO has an abundance of data theoretically at their fingertips, but they cannot do what they want with it.  The CMO needs to insist on, and work together with the CIO to build an overarching data strategy that meets their needs – both today and tomorrow because the marketing profession and tool sets are rapidly changing.  That means the CMO and their team need to step into a conversation they’ve never had before with the CIO and his/her team.  And it’s not about systems integration but it’s about data integration.
  • Existing Skill Sets:  The average marketer today is a right-brained individual.  They entered the profession because they are naturally gifted at branding, communications, and outbound perspectives.  And that requirement doesn’t go away – it’s still important.  But today’s marketer now needs to grow their left-brained skills, so they can take advantage of inbound information, marketing technologies, data, etc.  It’s hard to ask a right-brained person to suddenly be effective at managing this data.  The CMO needs to fill this skillset gap primarily by bringing in people that understand it, but they cannot ignore it themselves.  The CMO needs to understand how to manage a team of data scientists and operations people to dig through and analyze this data.  Some CMOs have actually learned to love data analysis themselves (in fact your CMO at Informatica Marge Breya is one of them).
  • Existing Relationships:  In a data-driven marketing world, relationships with the CIO become paramount.  They have historically determined what data is collected, where it is stored, what it is connected to, and how it is managed.  Today’s CMO isn’t just going to the CIO with a simple task, as in asking them to build a new dashboard.  They have to collectively work together to build a data strategy that will work for the organization as a whole.  And marketing is the “new kid on the block” in this discussion – the CIO has been working with finance, manufacturing, etc. for years, so it takes some time (and great data points!) to build that kind of cohesive relationship.  But most CIOs understand that it’s important, if for no other reason that they see budgets increasingly shifting to marketing and the rest of the Lines of Business.
  • Governance:  Who is ultimately responsible for the data that lives within an organization?  It’s not an easy question to answer.  And since marketing is a relatively new entrant into the data discussion, there are often a lot of questions left to answer. If marketing wants access to the customer data, what are we going to let them do with it? Read it?  Append to it?  How quickly does this happen? Who needs to author or approve changes to a data flow?  Who manages opt ins/outs and regulatory black lists?  And how does that impact our responsibility as an organization?  This is a new set of conversations for the CMO – but they’re absolutely critical.

______________________________________________

Q:  Are the CMOs you speak with concerned with measuring marketing success?

A:  Absolutely.  CMOs are feeling tremendous pressure from the CEO to quantify their results.  There was a recent Duke University study of CMOs that asked if they were feeling pressure from the CEO or board to justify what they’re doing.  64% of the respondents said that they do feel this pressure, and 63% say this pressure is increasing.

CMOs cannot ignore this.  They need to have access to the right data that they can trust to track the effectiveness of their organizations.  They need to quantitatively demonstrate the impact that their activities have had on corporate revenue – not just ROI or Marketing Qualified Leads.  They need to track data points all the way through the sales cycle to close and revenue, and to show their actual impact on what the CEO really cares about.

______________________________________________

Q:  Do you think marketers who undertake marketing automation products without a solid handle on their data first are getting solid results?

A:
  That is a tricky one.  Ideally, yes, they’d have their data in great shape before undertaking a marketing automation process.  The vast majority of companies who have implemented the various marketing technology tools have encountered dramatic data quality issues, often coming to light during the process of implementing their systems. So data quality and data integration is the ideal first step.

But the truth is, solving a company’s data problem isn’t a simple, straight-forward challenge.  It takes time and it’s not always obvious how to solve the problem.  Marketers need to be part of this conversation.  They need to drive how they’re going to be managing data moving forward.  And they need to involve people who understand data well, whether they be internal (typically in IT), or external (consulting companies like Crimson, and technology providers like Informatica).

So the reality for a CMO, is that it has to be a parallel path.  CMOs need to get involved in ensuring that data is managed in a way they can use effectively as a marketer, but in the meantime, they cannot stop doing their day-to-day job.  So, sure, they may not be getting the most out of their investment in marketing automation, but it’s the beginning of a process that will see tremendous returns over the long term.

______________________________________________

Q:  Is anybody really getting it “right” yet?

A:  This is the best part… yes!  We are starting to see more and more forward-thinking organizations really harnessing their data for competitive advantage, and using technology in very smart ways to tie it all together and make sense of it.  In fact, we are in the process of writing a book entitled “Moneyball for Marketing” that features eleven different companies who have marketing strategies and execution plans that we feel are leading their industries.

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So readers, what do you think?  Who do you think is getting it “right” by leveraging their data with smart technology and truly getting meaningful an impactful results?

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Posted in Big Data, CMO, Customer Acquisition & Retention, Operational Efficiency, Vibe | Tagged , , , , , , | Leave a comment

Takeaways from the Gartner Security and Risk Management Summit (2014)

Last week I had the opportunity to attend the Gartner Security and Risk Management Summit. At this event, Gartner analysts and security industry experts meet to discuss the latest trends, advances, best practices and research in the space. At the event, I had the privilege of connecting with customers, peers and partners. I was also excited to learn about changes that are shaping the data security landscape.

Here are some of the things I learned at the event:

  • Security continues to be a top CIO priority in 2014. Security is well-aligned with other trends such as big data, IoT, mobile, cloud, and collaboration. According to Gartner, the top CIO priority area is BI/analytics. Given our growing appetite for all things data and our increasing ability to mine data to increase top-line growth, this top billing makes perfect sense. The challenge is to protect the data assets that drive value for the company and ensure appropriate privacy controls.
  • Mobile and data security are the top focus for 2014 spending in North America according to Gartner’s pre-conference survey. Cloud rounds out the list when considering worldwide spending results.
  • Rise of the DRO (Digital Risk Officer). Fortunately, those same market trends are leading to an evolution of the CISO role to a Digital Security Officer and, longer term, a Digital Risk Officer. The DRO role will include determination of the risks and security of digital connectivity. Digital/Information Security risk is increasingly being reported as a business impact to the board.
  • Information management and information security are blending. Gartner assumes that 40% of global enterprises will have aligned governance of the two programs by 2017. This is not surprising given the overlap of common objectives such as inventories, classification, usage policies, and accountability/protection.
  • Security methodology is moving from a reactive approach to compliance-driven and proactive (risk-based) methodologies. There is simply too much data and too many events for analysts to monitor. Organizations need to understand their assets and their criticality. Big data analytics and context-aware security is then needed to reduce the noise and false positive rates to a manageable level. According to Gartner analyst Avivah Litan, ”By 2018, of all breaches that are detected within an enterprise, 70% will be found because they used context-aware security, up from 10% today.”

I want to close by sharing the identified Top Digital Security Trends for 2014

  • Software-defined security
  • Big data security analytics
  • Intelligent/Context-aware security controls
  • Application isolation
  • Endpoint threat detection and response
  • Website protection
  • Adaptive access
  • Securing the Internet of Things
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Posted in Big Data, CIO, Data Governance, Data Privacy, Data Security, Governance, Risk and Compliance | Tagged , , , , , , , , | Leave a comment

Reflections of a Former Analyst

In my last blog, I talked about the dreadful experience of cleaning raw data by hand as a former analyst a few years back. Well, the truth is, I was not alone. At a recent data mining Meetup event in San Francisco bay area,  I asked a few analysts: “How much time do you spend on cleaning your data at work?”  “More than 80% of my time” and “most my days” said the analysts, and “they are not fun”.

But check this out: There are over a dozen Meetup groups focused on data science and data mining here in the bay area I live. Those groups put on events multiple times a month, with topics often around hot, emerging  technologies such as machine learning, graph analysis, real-time analytics, new algorithm on analyzing social media data, and of course, anything Big Data.  Cools BI tools, new programming models and algorithms for better analysis are a big draw to data practitioners these days.

That got me thinking… if what analysts said to me is true, i.e., they spent 80% of their time on data prepping and 1/4 of that time analyzing the data and visualizing the results, which BTW, “is actually fun”, quoting a data analyst, then why are they drawn to the events focused on discussing the tools that can only help them 20% of the time? Why wouldn’t they want to explore technologies that can help address the dreadful 80% of the data scrubbing task they complain about?

Having been there myself, I thought perhaps a little self-reflection would help answer the question.

As a student of math, I love data and am fascinated about good stories I can discover from them.  My two-year math program in graduate school was primarily focused on learning how to build fabulous math models to simulate the real events, and use those formula to predict the future, or look for meaningful patterns.

I used BI and statistical analysis tools while at school, and continued to use them at work after I graduated. Those software were great in that they helped me get to the results and see what’s in my data, and I can develop conclusions and make recommendations based on those insights for my clients. Without BI and visualization tools, I would not have delivered any results.

That was fun and glamorous part of my job as an analyst, but when I was not creating nice charts and presentations to tell the stories in my data, I was spending time, great amount of time, sometimes up to the wee hours cleaning and verifying my data, I was convinced that was part of my job and I just had to suck it up.

It was only a few months ago that I stumbled upon data quality software – it happened when I joined Informatica. At first I thought they were talking to the wrong person when they started pitching me data quality solutions.

Turns out, the concept of data quality automation is a highly relevant and extremely intuitive subject to me, and for anyone who is dealing with data on the regular basis. Data quality software offers an automated process for data cleansing and is much faster and delivers more accurate results than manual process.  To put that in  math context, if a data quality tool can  reduce the data cleansing effort  from 80% to 40% (btw, this is hardly a random number, some of our customers have reported much better results),  that means analysts can now free up 40% of their time from scrubbing data,  and use that times to do the things they like  – playing with data in BI tools, building new models or running more scenarios,  producing different views of the data and discovering things they may not be able to before, and do all of that with clean, trusted data. No more bored to death experience, what they are left with are improved productivity, more accurate and consistent results, compelling stories about data, and most important, they can focus on doing the things they like! Not too shabby right?

I am excited about trying out the data quality tools we have here at Informtica, my fellow analysts, you should start looking into them also.  And I will check back in soon with more stories to share..

 

 

 

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Posted in Big Data, Business Impact / Benefits, Customers, Data Governance, Data Quality, Hadoop, Healthcare, Life Sciences, Profiling, Retail, Utilities & Energy | Tagged , , , , , , | Leave a comment