IBM’s Information on Demand (IOD) 2012 conference held in October at the Mandalay Bay in Las Vegas was like a theme park for information management. It’s one of the best forums for creating and experiencing the buzz among business partners, industry activities and customers. At this event, there were just as many new questions created as ones that were answered.

The event included three days of keynotes and nearly 700 breakout sessions which shared huge amounts of concentrated and comprehensive insights into Big Blue’s latest innovations, strategies and technologies. A multi-day pass to the event is barely enough to absorb the overwhelming amount of information and opportunities available. The daily sessions focused on the “how” and the “what” of solutions delivered by service-oriented architecture (SOA), data virtualization, cloud offerings, analytics, visualization and information management.

Not surprisingly, the strongest pillar at this year’s IBM IOD was big data. Everywhere we turned there was someone discussing or explaining the topic. Over the last few years there has been too much buzz and “scare” around businesses being “out of control” in the face of an insurmountable, unalterable tsunami of data. In one of his keynote sessions at IOD, Steve Mills, IBM’s SVP of Software, accurately stated “Big data was always there – it was summarized and archived. What’s new is the ability to take action on the data.”
Jason Silva opened  his session with a very polished presentation on digital connectivity and how it can relate to big data, Robert Leblanc continued with the theme but got more specific on how companies can use big data to help them deliver real value, while Deepak Advani continued with even more specific examples, highlighting some interviews with customers who had real-world implementations of IBM products and services, including InfoSphere Streams.

New offering announcements included IBM InfoSphere BigInsights Hadoop-based offering, specifically built-in social media analytics accelerators to help marketers develop applications for customer acquisition and retention, perform customer segmentation and campaign optimization, and streamline lead generation.  Additionally, the newly added Data Explorer feature enables advanced data federation capabilities from IBM’s Vivisimo acquisition.  The software automatically discovers and navigates available data wherever it resides to reveal themes, visualize relationships, identify the value of data and establish context of data usage.

On the Cloud and Virtualization front, a new offering included cloud analytics for line of business and SMBs — cloud-hosted applications to deliver predictive analytics specifically aimed at the financial services, retail, and education industries.

Another new solution called Analytic Answers offers SMBs predictive analytics as a service.  Customers will have access to tools, pre-built models, and expertise to help them develop actionable insights to their data stores.

So what does this all mean? To put it into perspective, we’re watching data traffic grow exponentially right before our eyes.  But it isn’t about the data; it’s about the path to wisdom that it provides. As organizations are swimming in an expanding sea of data that is either too voluminous or too unstructured to be managed and analyzed through traditional means, IOD certainly helped its hungry or rather confused customers catch a glimpse of its big data solutions and optimistic customer testimonials.

The IOD 2012 conference was an enriching experience where we got to spend time interacting with other customers and IBM experts. Hands-on labs and deep dive discussions allowing for a better understanding of product sets, is not something you’d get from a webinar or presentation material. Additionally, with the breadth of client base that were attracted to this event, the event served as an excellent networking platform.

With one’s brain nearing capacity by the end of the event, IBM’s IOD 2012 certainly left us a lot to think about. We can’t help but wonder, are we just mandated to think Big or is it truly our Big Future?

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As eCommerce (eAnything for that matter), Web 2.0 and several other buzzwords have come and gone, one of today’s biggest tech/business buzzword is “Big Data.” While the term itself continues to gain momentum in the media (see the Google trends graphs below), the technology has not caught up with the hype and the hype is fading to the point that Gartner has placed it in the “trough of disillusionment”.

Google Trends view of “Web 2.0”

Google Trends view of “Big Data”

Even though the buzz is fading, companies’ attention needs to focus on the business value derived from analytics and Big Data. So, what does this mean for the enterprise considering a deployment of Big Data technologies such as NoSQL and Hadoop?

1. Is it “Big?” – First, is your data and analytics project “big?” – In many ways, Big Data is really a misnomer. Big implies a large quantity of data, and while managing large quantities of Big Data is certainly a use case of many Big Data solutions, it really is only one. Data can be more than big; it’s fast, constantly changing and noisy. Technologies branded as Big Data can help manage these areas as well – from real time processing to fast storage and retrieval technologies; these technologies can help the enterprise adapt to “changing data.”
2. Which Processes will Improve? – What are the areas of opportunity for additional data-driven insights in your enterprise? Both business and IT leaders need to take a hard look at the analytics initiatives they are spearheading. Will the additional insight coming from these new systems truly make an impact on existing business processes and transform them? Or, will they generate additional reports in managers’ inboxes that will go unread?
3. What Return will the Project Drive? – As with any other major IT initiative, Big Data analytics projects should be tied to a tangible business outcome. Ensure the investment in both IT and in employees merits a positive financial return. Analytics for analytics sake benefits no one.
4. “Think Globally; Act Locally”- Big Data initiatives can get big very quickly. As with any other analytics initiative, “eating the elephant” and building a robust Big Data platform with thousands of Hadoop nodes isn’t necessarily the right approach. Enterprises looking to make a play in the Big Data analytics space should look for quick wins and prioritize those plays which will deliver the most bang for the buck. Big Data analytics is a journey and no journey is successful if you cannot take a successful first step.
5. Find Internal Champions and Build Buzz. Once you start gaining success with your Big Data initiatives, make sure you leverage those people whose processes you’ve influenced. Hopefully, their day-to-day jobs will be simpler, faster, more informed; fulfilling the prophecies of “Big Data.” Make sure you capture their testimonials as you look to move onto bigger opportunities as internal references are usually the most credible.

These five steps may not look too unfamiliar to most who have been around the analytics space for more than a few years. “Big Data” technologies do promise and have often delivered some revolutionary benefits to many firms who have deployed them. If firms can create simple, clear plans to implement the unique attributes of Big Data technologies, they will be well-equipped to deliver the benefits the current hype suggests.

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In the UK the Water industry operates on a geographic basis. If you live in a water company’s supply area, then that company bills you for the supply of fresh water and treatment of waste water. In the electricity and gas industry the retail market is more open and non-geographic, so you can have a billing relationship with a company from a wider range of energy suppliers even though your local network is owned by only one.

This is one of the reasons why the water regulator Ofwat imposed its Service Incentive Mechanism on water companies, where they are assessed on a number of customer satisfaction metrics, ranked and rated accordingly, with lower ranked suppliers mandated to improve their customer service rather than raising their pricing. In a non-competitive market the regulator, who has consumers’ interests at heart, has ensured that water companies have a strong incentive to improve customer service.

In the gas and electricity markets with open competition there was a drive to attract new customers and retain existing customers through a large number of pricing tariffs that were changed regularly. This made it hard for consumers to compare the offers from various suppliers. The regulator Ofgem, as part of its recent Retail Market Review, has mandated that energy suppliers can only offer four tariffs. In such a competitive market, being unable to differentiate as much from a sales standpoint has focused attention onto service delivery, and on improving customer experience as a way to attract and retain customers.

Water, gas and electricity companies are now all eagerly looking at ways to improve customer experience, and there are valuable lessons to be learnt from other retail markets that have already tackled this issue. Being able to offer a good customer experience is predicated on knowing your customer, having a 360 degree view of them, understanding all of their interactions with you, and even understanding how best to communicate with them. This in turn needs a customer data management system where the master record for a customer is held, and which can be enriched over time by every interaction you have with that customer.

While many suppliers have implemented large enterprise resource planning systems, the ability to address customer data management as above using these existing systems is limited, as they are often slow and hard to change.

There are also other factors to consider which are occurring at an ever increasing rate. There is a millennial generation of customers now who expect companies to respond to them immediately, who want the ability to self-serve, who interact and recommend or criticize through social media channels. This is a generation that has been heavily marketed to by brands, and are very brand conscious.

In addition there is the advent of smart metering, where suppliers are going to be measuring consumption on a granular basis, to help optimise supply and transmission networks, educate consumers on their consumption, and to provide other benefits.

All of the above suggests the need for a Customer Data Management platform, which can create rich customer data records over time, so that suppliers understand how to engage with their customers, what is important to them, and ensure that other business IT systems leverage this data so that every interaction a customer has with the supplier (whether through a printed letter, the contact centre, a service call-out and so on) appears seamless and consistent, where the consumer really feels that the supplier understand who they are, and best serves their needs.

For the Millennial generation, this is even more important, as they tend to group together on brands, building consensus through social media. A supplier that really understands every aspect of each of their customers is going to offer a better customer experience than one that doesn’t, and attract new customers from that generation.

Drilling down to the technology level, suppliers may have several customer data records for the same unique customer, from previous billing relationships with them, from customers moving from address to address where the current ERP systems are not updating a golden master but creating a new customer, or from different databases they use to market to customers and solicit feedback from them. This is where an MDM platform fills the need, providing that set of golden master data that all downstream customer relevant business IT systems can draw on.

Bringing together and making sense of all of the consumption data from smart metering is best addressed by a big data platform, and just as importantly the ability to exploit that data by finding the right “needle in a haystack” through analytics is required. This feeds into the Customer Data Management platform, further enriching relevant customer data records.

The days are long gone when consumers of water, gas and electricity had to live with what their service provider wanted to offer. In this ever-changing world of aggressive competition, demanding consumers, rapid technology advances and stringent regulatory requirements, the utility companies that succeed will be the ones that adapt quickly to changes in consumer needs and behaviour, and more importantly deliver an enhanced customer experience. And an effective customer data management strategy that helps in gaining valuable consumer insights will be the crucial differentiator for a utility company to maintain its market position or improve it!

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The well-established perception that data security is a combination of people, process and technology holds good for “Big Data”, as well as any other kind of data. In the “Big Data World” data security gets more complex and magnified, but the fundamental issues remain the same. Recent studies reveal that the total average cost of data breach can be as high as $5.4 million for a given financial year, this is a significant number. This alone is reason enough for organisations to evolve from traditional data governance mechanisms to models that can encompass Big Data as well.

This poses some big questions for Big Data advocates to ponder over though – is it moral, or legal, to make use of Big Data in such a way that it reveals information about someone, or something that may not be intended for public consumption? For example, if someone has an illness, or is carrying out activities that they don’t want others to know about? For enterprises, they should consider whether this information can lead to unintended consequences and potential data security breaches? Should a retailer be taken by surprise if they are penalised for using customer data that they have derived, for purposes unknown to the individual about whom they have collected it? Aside from morality, given the financial impact of data breaches, this requires serious thought.

An immediate action item stems from this: classification of “derived data”. Just because the sources you use to derive information are classified as freely available, does this mean that the information you have derived should also be classified as freely available? It is imperative for organisations to understand this and then classify the information from analytics, just as robustly as the information used to derive it.

This brings us to a systemic approach:

• Understand the “business outcome” that you are looking to influence through Big Data technologies, and categorically list out the value you intend to derive out of their use.
• Understand the “derived data” that you are looking to use to influence this outcome, classify it for security purposes, understanding that this classification may be different from the sources that are used to derive it.
• In other words, treat the outcome of Big Data as an information source in its own right and protect it accordingly.

Now that you have done this, given that issues can easily get out of hand, how do you optimise Data Governance processes to make them more robust? Here are some ideas:

• Increase the frequency of data monitoring. For example, with the arrival of gadgets such as smart meters, data that was once captured every month can now be captured every 10 – 15 minutes – monthly monitoring is no longer enough!
• Address data quality issues at similarly increased frequencies.
• Bring in robust data governance policies and procedures for stricter access controls and privacy restrictions on the resulting data sets.

These actions can not only help you comply with regulatory requirements on one hand, but can also help prevent security breaches that can cost heavily by way of negative publicity, lawsuits and fines.

With Big Data promising big opportunities, it is more important than ever for organisations who intend to monetise it to be extremely cautious and not fall foul of stringent data laws and compliance.

Looking forward, companies need to get hold of this issue and ensure they are securing data in the correct way – before their Big Data breach becomes the next Big Headline.

The article was originally published on Big Data Republic on December 30, 2013 and is re-posted here by permission.

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The billing (claims) data of healthcare providers for the United States Medicare Program, which is considered to be one of the most important healthcare programs to be held private for almost 35 years, was made available to the public on April 9, 2014.

The data that will be available to the public includes the identifiable healthcare provider information, specialty, procedure and associated cost information. However, data related to the patients will not be available to the public, as patient privacy will be maintained.

Experts and some groups have opposed the release of this data due to the possibility of privacy intrusion and potential for patients and payers to misinterpret the data among others. The implications of this data release require further extensive analysis.

Several payer groups are very excited about this information. Government and the government agencies are interested to see the cost efficiencies that this program would bring. Insurance firms and private payers would like to leverage this information to benchmark the claims the providers make. Patients and healthcare delivery enablers (employers) would like to wean the ‘quality of care’ information from this data.

This data is really a treasure trove to understanding the various dimensions of government spending, provider billing patterns, fraud and potential cost efficiencies. In my opinion, three product ideas based on this data which can be features of a stand-alone product or used in conjunction with any existing product are:

• Fraud/Improper payment prevention
• Doctor ratings
• Driving cost efficiencies and cost reductions

Fraud/Improper payment prevention
According to a US Government Accountability report, in 2012, the Medicare program covered more than 49 million elderly and disabled beneficiaries. The cost was $555 billion and the estimated improper payments reached $44 billion. This ratio of almost 8% of the payments being improper provides a great opportunity for reduction.

A product which uses advanced prediction/estimation models based on patient behavior, billing cycles, nation-wide provider billing estimates and standard cost estimates to detect improper payments would be of great interest to the government.

Insurance providers would also be very interested in such a product.

Doctor ratings
With the newly available information on the procedures, it will become easy to identify the expertise of the providers. For example, if one wanted to get a cataract operation done, you could find out how many operations your surgeon did last year. Research shows that the quality of procedures are often better if the doctor performs it frequently.

A product or a feature which combines these newly available doctor ratings with the cost information would be a very powerful tool for the selection of the expert. Several insurance companies and healthcare analytics firms already provide features which list doctors by their specialty, ratings gathered from peers, and cost estimates. The new analyzed data would fit right in. A product which compares the healthcare provider claims estimate for the same procedure from a Medicare perspective and insurance provider perspective would be of great interest to both the government agencies (CMS etc.) as well as the insurance providers. This will help the payers to benchmark costs and drive efficiencies.

Driving cost efficiencies and cost reductions
With the data on the cost per procedure now clearly available, researchers can look at the money spent on drugs or procedures in different parts of the country. They can check whether that leads to increased quality in care. From a payer point of view, now there is comparable claims information for the provider to compare the insurance coverage part and the Medicare billing cost for the same procedure.

A product which compares the healthcare provider claims estimate for the same procedure from a Medicare perspective and insurance provider perspective would be of great interest to both the government agencies (CMS etc.) as well as the insurance providers. This will help the payers to benchmark costs and drive efficiencies.

In summary, the released Medicare claims data is a very rich piece of data that will make its presence felt in multiple facets of healthcare and its repercussions will be tremendous to say the least.

The article was originally published on Healthcare Payer News on April 24, 2014 and is re-posted here by permission.

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It’s well known that it’s more cost effective to sell to existing customers, than it is to acquire new ones. If businesses can gain extra insights into how their customers use their services, they can find new ways to tailor and customise those services to individual customers. Electronic payments make the interaction with a business a far smoother, quicker and convenient experience for customers – tapping into that by implementing electronic payment methods can have a real pay-off. One example of an electronic payments success story is from Starbucks, which claims that more than 10% of its in-store sales are driven by its mobile wallet app.

The data acquired through electronic payment systems, and the many mobile transactions that are happening through payments apps are a gold mine of customer behaviour. A few payment providers are using this data to analyse spending patterns based on the locations and pro-actively engaging with the customers. One has to only look at a company like PayPal, that has over 100 million credit cards on file, to start to understand exactly how much data electronic payment providers hold and how valuable that data might be, given that it can offer insights into such a large numbers of customers’ behaviour.

Using the data gleaned from payment systems, banks and payment providers can better understand the spend pattern and location of consumer behaviour, which if acted upon can present opportunities to increase customer spend and loyalty at the same time. Here’s a scenario for increasing loyalty amongst commuters – you could have a payment app that analyses the data of the various journeys a person takes on a tube. If that customer was to travel a particular route every day of the week, the app would use this data to work out the best possible cost options; a Weekly Pass, Monthly Pass, or PAYG Oyster. The payment provider’s app could then ping the customer with the most cost effective option, which could be purchased through the same app. In an example like this everyone wins, the traveller saves money and it greatly increases his loyalty towards the payment provider.

While using the data from electronic payment systems cans boost loyalty, there is no magic formula to it – loyalty will always be about trust and advice. But what banks and payment providers have been able to tap into is the use of data analytics and mobile payment analytics through geospatial applications and innovative partnership models. Using technology to create innovative rewards for customers, banks and financial providers can build all-important trust by saving the customer money.

The article was originally published on The Digital Banking Club on July 29, 2014 and is re-posted here by permission.

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The market is abuzz with data trends like big data and data science, and there are more to come. Numerous organizations across the world are trying to find the best possible way to establish an effective data group. However, organizations face various challenges in setting up a best-in-class data group, and those who have somehow managed to set one up face issues sustaining it. Hence, it has become very critical to analyze and understand the factors responsible for making this task challenging.

One of the key reasons for the nascent death of such a data group is the incapability of data organizations to continuously showcase their real potential to a business. These groups usually have technologists and evangelists who build on existing success and take on more volume and velocity of data, variety of data types—including structured, unstructured, and semi-structured—and provide real-time data processing and analytics capabilities.

The organization keeps the group and the hype stays on for a while until interest dwindles. Due to lack of adoption by business to sustain growth and interest, such data groups slowly cease to exist.

This article highlights the ways that help with the setup and sustenance of a data organization—focusing on five must have areas for this group. Going with the current trends, this group is referred as the data sciences group.

The Mission
The mission of a data sciences group spans a variety of priorities—providing protection, value, predictability, accuracy, and easy access.

The first goal is to provide solutions that help eliminate the vulnerability of business, such as loss of customers, inappropriate transfer of sensitive data, and market threats.

Secondarily, they should offer valuable solutions to keep a constant watch on the multi-channel customer voice and adapt to the pulse of a business’ biggest asset, the customer.

Thirdly, it should provide solutions to help businesses make faster and more accurate decisions using prediction models to eliminate human error and create a data-driven organization with veracity.

It should also offer solutions to help lower a business’ total cost of operations es using disruptive technologies and eliminating technical debt.

Finally, it should provide solutions that are be easily accessible and available anywhere, everywhere, and any time.

Group Composition
A data sciences group should be made up of a business analyst; a data analyst; technical resources with knowledge of mobility, visualization/user experience, and big data technology; and a business sponsor.

Every project this group executes must have a sponsor from the business who spends time ensuring that the solution being developed delivers value.

In terms of deliverables, the business analyst must be able to create a product requirements document for the solution, road map, ROI, and benefits to the business. The data analyst must be able to point to source and the target data deliverables and be responsible for the data quality of the solution. The technical resources own the technical solution and architecture. The business sponsor takes responsibility the deliverable to make the solution operational in the organization. In terms of training, the data sciences group needs to constantly learn and keep pace with the technological advances so that the solutions developed are innovative.

Data Sciences Group’s Evangelization
A data sciences group can only be evangelized by the ultimate operational and analytics users of the solution in question. They are the people who can keep this group from disappearing into oblivion and they do it by integrating the newly developed solutions and adopting them. The most important thing to note is that this group should not be confused with the enterprise data warehouse group.

The article was originally published on Software Magazine on November 26, 2014 and is re-posted here by permission. 

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Industry leaders are debating the co-existence of big data & traditional business intelligence. One group strongly believes traditional business intelligence will be washed away in the big data tsunami, while another group discounts big data as a big hype and vouches for traditional business intelligence as an organization’s bastion.

Big data will continue to be the buzz word at least for a few more decades, as the industry going all out to create interesting solutions. With third party solution providers fighting for their share of pie, Apache incubator will continue grooming tons of new frameworks & tools. New players will emerge and existing niche players will be consolidated – either by acquisitions or by getting lost in a fierce battle. Big data is here to stay for a long time, growing in stature and adding value to customers.

Defined in simple terms, business intelligence is to use reports and dashboards to answer a set of formulated questions. Today, big data solutions are augmenting the data warehouse eco-system; solving problems that are not optimally solvable otherwise due to the sheer volume, velocity and variety of data and other factors.

Big data solutions can do much more to help businesses. True potential arises with the ability to ask bigger questions by comprehending data beyond the realms of the traditional data warehouse. Data discovery starts with asking questions that are not asked today to know the unknown … Voilà!

Big data can catalyze organizations to mature the curve from business intelligence to data discovery. An emerging area in big data is graph database and allied graph analytics. Graphs are not new, they were documented as early as 1736 on the famous Seven Bridges of Königsberg problem presented by Leonhard Euler and have evolved since then. Today graph databases are imperative in the NoSQL arena, when the relationship among the data is as important as the data itself. The data is stored in a schema-less manner as nodes and edges, with ability to crunch humongous data in memory at astonishing speeds. Graph analytics provide the ability to solve the most complex of problems in a simple yet efficient manner by applying graph theories.

The methodology of overlaying a subset graph pattern over the dataset towards discovery of matching data patterns makes data discovery engaging as well as an easy activity.

Majority of the product recommendation engines for online retail today have products hardwired at the platform configurations. These recommendations are usually pre-calculated weeks ahead based on customer’s historic purchases and may be irrelevant today. Graph analytics can provide dynamic recommendations based on real-time or near real-time buying patterns using pattern matching algorithms which not only uses past purchases but also product catalog search patterns and social media activities.

Data discovery’s impact on enterprises bottom line is immense and big data solutions can aid getting there faster. Life science and financial industries are early adopters where solutions are helping drug repurposing, detecting financial fraud patterns and money laundering.

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Data, as we know, is a collection of facts or information. Historically, data has been collected in many forms. After the advent of technology, data now is mostly found in PDFs and other related formats. We may have data stored in tables, but eventually, when it is to be published, it needs to take shape in some document form. These documents can be difficult for organizations to understand and often don’t allow for the proper analysis of the data.

Recent technological advancements however, have made it easier to assemble data so that it can be analyzed more effectively. Unfortunately, there is not one single product on the market that is an off-the-shelf solution for this problem. Instead, one has to use a varied and diverse combination of products and custom solutions to achieve the business objective of storing this data in some relational model, so that proper analysis can become possible and quick.

When selecting these products one must keep in mind the following challenges so that they create the best customized solution for their needs.

1. Data Sourcing: This section deals in collection capabilities of the tool or the solution being chosen. One must consider how easy it is to make the implementation happen, how tools are collecting data, and whether or not it will allow crawling authenticated sites along with the public sites or only crawls public sites. Even if the tool has the provision to crawl, certain vendors may block anonymous backdoor programs to walk into their property and start crawling. If the security system in the vendor servers is stringent, even if we try to mimic it with the correct user credentials and try to make an entry through the back-door, the requests will still get declined, because of central authentication servers at vendor sites. You must ask yourself if you have an agreement in place with vendors having stringent authentication security models for allowing our requests to go through.
2. What to crawl and what not to crawl: For data collection, it is one of the biggest challenges to figure out what to crawl and what not to crawl. Business users have to make a crystal clear distinction between what information of data is needed and what type of documents they would be available in. As vendors are going to post any kind of documents in their repositories, it is essential to see which ones are needed.
3. Data Extraction: This is again one of the most challenging areas of extracting data points from the document and building an association of the data point. This delivers an accelerated business value to the organization post data analysis. Most organizations falter in making an informed decision in this area. There are no off-the-shelf products available. One of the products, which I have worked with is the Apache PDF box. This product is able to read the pdf cell by cell as it identifies the information that is residing in a cell. After the information is extracted, a custom application needs to build a proper relational model, which builds the relationship for the data point extracted with its taxonomy structure. Data point mapping with respect to its context after the extraction, is a huge challenge. The data in a pdf may have a layout which seems fine to the naked eye, but when read from the pdf using a PDF Box, the true storage pattern can be obtained.
4. Data Format during PDF creation: One has to keep in mind how PDFs are created. This is one of the challenging areas, when different data sources have been used to create the PDF documents. Your products and custom solutions might even have to be enhanced depending on how the PDF was generated. Sometimes PDFs are generated for distribution using Excel sheets, other times they could come from Word documents or table sources. Such diverse sources for creating distribution documents results in additional format cells coming into the PDF. Sometimes the data can also be in an image format.
5. Building a rectification tool: Data points are extracted through a variety of process, therefore one needs a tool to rectify the data manually by the data analysts. This is especially important because data that is extracted incorrectly or wrongly associated, will not convey any meaning to the business users. Extracting such huge varieties of data points and then pulling them together with a tool can expose a big challenge in terms of performance, since you have to map the relational model for each data point.

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Most enterprises spend a significant part of their IT budget on application development and maintenance (ADM). As per Gartner, ADM accounts for 34 percent of IT budgets. The cost grows as the technological complexity along with business processes and application size grows. IT managers focusing on reducing ADM costs face challenges from business owners looking for new features and processes, which can result in more complex and thicker applications. The more complex and thick the application, the higher the overall ADM cost. This increase in ADM cost coupled with constant pressure to reduce overall IT cost, forces IT managers to explore other avenues like predictive analytics.

More than 50 percent of ADM costs go into application maintenance and remediation due to ad hoc faults and failures. With teams spending the majority of their time on these critical failures, predictive analytics can drastically help to reduce costs. Some of these failures potentially bring the IT systems down resulting in business losses. IT managers have tried various techniques like regression testing and application monitoring to minimize the cost and losses from these failures. However when it comes to ADM, the majority of enterprises use staff augmentation, which can be problematic because few teams usually think beyond providing quick reactive fixes.

Thinking Ahead
Because the cost of fixing these failures can be significantly reduced if they are predicted in advance, predictive analytics becomes doubly important. This is not a new concept. Predictive analytics has already been used in various domains like banking, insurance, and retail. As in these industries, the majority of critical errors are driven by issues happening earlier.

For instance, a failure in a settlement system of an investment bank could be a result of a particular type of trade happening for the first time or an error that has already happened in upstream systems, such as trading and clearance applications. These errors in various systems can be associated to predict potential critical errors in downstream applications.

Another interesting example is database bottlenecks resulting in queries that run slower than usual. Due to the slow execution of these queries, the average number of concurrent requests—due to backlog—goes up. Eventually application servers run out of threads and hangs. This brings down the application. As we can see, this could be a common problem for many enterprises. Application down time can be reduced by proactively resolving associated issues, in this case database slowness.

A popular predictive analytics technique known as association, also called Market Basket Analytics, is ordinarily used by retailers, but can be applied to application health and behavior data to get insight on failures. Application analytics and monitoring platform can be constructed to reduce defects, and drive software and hardware optimization. The platform collects various types of log files that can be stored together in a clustered environment to avoid any space constraint. Data can be used to monitor applications in near-real time mode and also provide proactive recommendations for optimization and potential failures. The prediction of performance and failures can be made using analytics techniques such as association rules and regression.

Therefore, we can say that predictive analytics plays a vital role in finding out critical application failure areas well in advance, helping enterprises excel in the dynamic market landscape.

Though a lot of work still needs to be done in this area, predictive analytics will play a vital role in finding out critical application failure areas well in advance helping enterprises excel in the dynamic market landscape.

The article was originally published on Software Magazine and is re-posted here by permission. 

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In today’s world where data is collected at every interaction, be it over the phone, mobile, PC, sensors, with or without us knowing, it becomes important to have a strategy around data. Traditionally, data has been seen as something to “run the business,” but, in today’s context, it can actually “be the business” if monetized well. An example of Internet of Things (IoT) data in a customer context is the wristband one wears at amusement parks that provides real-time data about customer interaction at all times, and this data can be processed in near real time to push out relevant offers and alerts to enhance the customer experience. The question is: How do organizations prepare themselves to take advantage of data?

The key lies in building a modern data architecture that is open, flexible and scalable, something that can accommodate your existing data assets as well as potential new ones. Before we talk about specific steps to modernize data architecture, let’s look at typical challenges:

1. Many applications within the organization have been around for 20 or more years. While the usage for some of them is known, it is still not clear who is leveraging the data in each application and for what purpose. How do we find out?
2. To meet their reporting needs, organizations have built multiple data assets including data warehouses and data marts. Additionally, they have power users collating data from multiple sources and creating reports using MS Excel. Numbers are inconsistent and vary based on who is preparing them and the intended purpose.
3. Organizations have multiple applications and data assets starting with mainframe-based ones, client-server, Web applications and some newer cloud-based applications, all co-existing. They struggle to find the right people to support the applications, especially the older ones.
4. Organizations are aware of the new developments in the big data space including NoSQL databases and the Hadoop ecosystem, and have typically embarked on some initiatives to get started on this. The main challenge is around integrating this with the traditional data warehouse technologies.
5. People, and by extension, their skills, are the biggest assets of any organization. CIOs are concerned about having to find an army of programmers for populating Hadoop-based data repositories. The other big concern is how to leverage existing SQL skills, which people have acquired over the years.

These are valid concerns, and some are more applicable than others based on the context. Nonetheless, given the inevitable need to be able to better monetize data and modernize technology platforms, it is important to have a strategy. I recommend the following approach:

1. Data asset inventory: Create a complete list of data assets – legacy, data warehouses, data marts, data islands. Identify the data flows between these assets and the usage patterns. It might be particularly hard for some legacy systems, but this serves as the starting point for any consolidation and modernization.
2. Data asset rationalization: Based on the list of data assets and the usage, it is important to rationalize them. What this means is to identify if the same data is coming from multiple applications, and if so, which is the authoritative source, and which ones can be retired. This is a very important exercise and can help consolidate the number of data assets to a manageable few. In this context, master data management is critical to ensure you have good quality data.
3. Data lineage: Undertaking a data lineage exercise to identify data flows – creating detailed documentation especially for the legacy applications – is a must. This greatly reduces the risk of dependency on key personnel and also makes it easier to migrate to a future state architecture.
4. Data infrastructure: Have a big data and cloud strategy in place to bring in newer technologies in a pilot mode. Start with a non-legacy application to understand the technology, and move applications over in conjunction with data asset rationalization. The “data on cloud” is going to be an important component of modern architecture especially when dealing with IoT data.
5. Data technology: It pays to understand the different options available in a very crowded and rapidly evolving marketplace, and to select the right technologies that fit into your architecture from a technology standpoint as well as a people standpoint. For example, using a data integration tool with big data connectors will eliminate the need for people who can write MapReduce code.

Creating a holistic data strategy in light of changes in the business, and taking a structured approach, will definitely help lay a solid foundation that will be the basis for monetizing data.

The article was originally published in Analytics Magazine on August 27, 2015 and is re-posted here by permission.

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Customers today are spoiled when it comes to choices. They choose the products and services most relevant to them. Now, in order to stay relevant, banks need to get closer to their customers and understand them better than before, compared to their peers. Only then, through personalized and relevant offers, can banks significantly improve the success rates of their marketing campaigns.

In marketing, segmentation has been a fundamental building block of understanding customers’ behavior. As a concept, the advertising model core to today’s large search engines, social media networks, and e-commerce players is well-known to marketers at banks. However, they continue with traditional approaches based on demography, geography, and socio-economic classifications. Yet approach to segmentation has remained largely one or two dimensional. Getting reports and cross tab analysis on customers has been a struggle for most bank marketers for a long time until recently.

Micro-segmentation utilizes multiple dimensions to identify a set of customers, potentially pivoting around a business outcome – in this case the probability of the customers to accept an offer or buy a product.

Those of us who’ve had the opportunity to work in line functions, as well as technology at different times over our careers, know how technology has changed and become easier for even the untrained but curious user to undertake in terms of in-depth analyses versus what could be done 10 years ago. It has become very easy for the average business user to slice, dice and visualize data without almost writing a single line of code. It is not difficult for the curious users to come up with a matrix showing “people who have bought this, also bought that” using pre-built formula in spreadsheets. Hence, one may wonder why it is that marketers in retail banks are not delving deeper inside their traditional monolithic customer segments to uncover micro segments that will help them understand and serve their customers better.

The most common reasons provided by marketers about why they are not ‘micro-segmenting’ their customer base are as follows:

1. Yes, we know about it and understand it, but it is too complicated for us to implement.

Users today are slicing and dicing their data starting from cross-tabs and pivot tables using spreadsheets to having an in-house team of “data scientists” or people with the knowledge of sophisticated statistical and mathematical modelling techniques. Most banks are somewhere in-between. In such cases it is worthwhile to start with something simple and small. For example, start with two or three attributes to segment the customer base before moving onto using more attributes to identify customers by needs that your organization’s products or services can uniquely address. This essentially breaks down a large problem into smaller bits.

2. We cannot conceptualize how it will work with our data in our business context in a meaningful way to impact our business goals

This is an interesting view. As technology specialists, we have the access and know-how of the tools that make data analysis fast, efficient, and effortless. While we may possibly stumble upon interesting insights based on a bank’s data, the best way possibly would be to enable the business users with similar capabilities related to data analysis based on technology and tools. It goes like this: it is extremely difficult to create a stone sculpture using a toothpick rather than using an iron chisel. However, it is still the sculptor who make the fine piece of art and not the blacksmith who made that chisel. Technology specialists can demonstrate all that is possible based on their views of the bank’s business and operations and perhaps work together as a team to help the bank in this pursuit.

3. We do not have enough and meaningful data

This is still one of the common reasons cited by business users. This is relatively true given that organizations, however big or small, (apart from internet search giants, social media companies and the like whose entire business model is built on leveraging user data) do not have data consolidated and aggregated in a single place. Even large banks face this problem. With more and more transactions and interactions channels, this problem is further accentuated given a customer may have used an ATM, called the call center, browsed the mobile app, and dropped by the branch to inquire about a new product. Finally, from a data perspective, there is usually a gap in terms of capturing the unstructured interactions and conversations that customers have with their relationship managers or the bank channels. If decision makers do believe that using three attributes are better than two attributes in understanding and defining their customers better (e.g. overall assets with the bank, monthly aggregate transactions, most recent interaction with the bank), it is a significant step towards a micro-segmentation mindset. With the business users’ requirement clearly stated to their IT, i.e. getting all transactions, interactions, profile and expectation data of their customers in a single place, the key ingredients will now be available for a journey in micro-segmentation.

Banks possess a treasure trove of transaction data that is by far a more reliable source to understand customer needs compared to the casual posts and chatter on social media or the browsing and search patterns on websites. With the three basic perspectives outlined above, banks can significantly become more aware of their customers’ needs and offer relevant choices to them as a result. This will noticeably improve the outcome of their marketing campaigns.

The article was originally published on BankNews (July 2016) and is re-posted here by permission.

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In the UK the Water industry operates on a geographic basis. If you live in a water company’s supply area, then that company bills you for the supply of fresh water and treatment of waste water. In the electricity and gas industry the retail market is more open and non-geographic, so you can have a 

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The well-established perception that data security is a combination of people, process and technology holds good for “Big Data”, as well as any other kind of data. In the “Big Data World” data security gets more complex and magnified, but the fundamental issues remain the same. Recent studies reveal that the total average cost of 

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The billing (claims) data of healthcare providers for the United States Medicare Program, which is considered to be one of the most important healthcare programs to be held private for almost 35 years, was made available to the public on April 9, 2014. The data that will be available to the public includes the identifiable 

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It’s well known that it’s more cost effective to sell to existing customers, than it is to acquire new ones. If businesses can gain extra insights into how their customers use their services, they can find new ways to tailor and customise those services to individual customers. Electronic payments make the interaction with a business 

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Industry leaders are debating the co-existence of big data & traditional business intelligence. One group strongly believes traditional business intelligence will be washed away in the big data tsunami, while another group discounts big data as a big hype and vouches for traditional business intelligence as an organization’s bastion. Big data will continue to be 

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The market is abuzz with data trends like big data and data science, and there are more to come. Numerous organizations across the world are trying to find the best possible way to establish an effective data group. However, organizations face various challenges in setting up a best-in-class data group, and those who have somehow 

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Data, as we know, is a collection of facts or information. Historically, data has been collected in many forms. After the advent of technology, data now is mostly found in PDFs and other related formats. We may have data stored in tables, but eventually, when it is to be published, it needs to take shape 

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Most enterprises spend a significant part of their IT budget on application development and maintenance (ADM). As per Gartner, ADM accounts for 34 percent of IT budgets. The cost grows as the technological complexity along with business processes and application size grows. IT managers focusing on reducing ADM costs face challenges from business owners looking 

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