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Insurers have a lot of customer data across different systems peppered throughout their enterprise. Customer data would typically live in multiple systems like CRM, Billing, Policy Administration, and so on. This approach however suffers from multiple challenges:

 

1. Duplicate data across multiple systems
2. Multiple Versions of the same data point
3. No Single source of truth
4. No correlation between cause and action
5. Completely under utilized customer interaction data

 

Imagine a structure so flexible and scalable that, that it could bring all your data sources together, irrespective of the data formats, tie in with the customer key result areas (KRAs) and at the same time deliver predictable insights at the point of decision. In real time.

 

Enter single customer view. Or as we call it – Customer OneView.

 

OneView in Action

 

Customer OneView is part of Aureus data analytics platform called CRUX. OneView is not anything like a CRM. While a CRM would show only static information, OneView delivers intelligent, usable and real time insights that can be put to use immediately.

OneView can integrate with (atleast) four broad event data streams:

1. Customer
2. Relationship
3. Transactions
4. Interaction

 

Nitin had written about stream based data integration on his insightful post titled “Cheers to Stream Based Integration“

 

These data streams could originate across multiple data systems – Policy Admin, CRM, Billing, etc.. Between them, these four cover some of the most critical customer data, that often lies under utilized. OneView not only brings these data streams together, but it also helps build a comprehensive customer life journey showing important milestones, critical customer interactions, sentiment at each interaction or transaction level as well as at a relationship level.  While OneView is a powerful insights delivery framework, it also helps to deliver the output of predictive analytics models in a form that is usable by the business users. OneView can help translate the output of the analytical models into usable insights. Imagine a customer sales representative talking to a customer, or a field sales agent going to meet a customer. OneView will give them unambiguous insight into the customers history, sentiment and even potential  action to take, without burdening them with the Hows and Whys.

 

Imagine a typical customer cross sell scenario. Most organizations tend to throw (figuratively speaking) the entire product catalog at the customer without any consideration for their lifestage needs, portfolio, demographics etc… Not only is this a highly ineffective cross sell approach, but it is a terrible customer experience approach. With OneView the customer service representative or the field service agents knows exactly what the customers latest and overall sentiment is, what her product portfolio looks like and which product the customer is most likely to buy.

 

The end goal of any activity is to make the end customers experience epic. By knowing how a customer is likely to behave, modeled on her previous behavior, insurance companies can ensure that the customer experience is always moving to the right.

 

Source: A Single Customer View : The Secret Weapon Everyone Must Use by analyticsweek

The surging interest in blockchain initially pertained to its utility as the underlying architecture for the cryptocurrency phenomenon. Nonetheless, its core attributes (its distributed ledger system, immutability, and requisite permissions) are rapidly gaining credence in an assortment of verticals for numerous deployments.

Blockchain techniques are routinely used in several facets of supply chain management, insurance, and finance. In order to realize the widespread adoption rates many believe this technology is capable of, however, blockchain must enhance the very means of managing data-driven processes, similar to how applications of Artificial Intelligence are attempting to do so.

Today, there are myriad options for the enterprise to improve operations by embedding blockchain into fundamental aspects of data management. If properly architected, this technology can substantially impact facets of Master Data Management, data governance, and security. Additionally, it can provide these advantages not only between organizations, but also within them, operating as what Franz CEO Jans Aasman termed “a usability layer on top” of any number of IT systems.

Customer Domain Management
A particularly persuasive use case for the horizontal adoption of blockchain is deploying it to improve customer relations. Because blockchain essentially functions as a distributed database in which transactions between parties must be validated for approval (via a consensus approach bereft of centralized authority), it’s ideal for preserving the integrity of interactions between the enterprise and valued customers. In this respect it can “create trusted ledgers for customers that are completely invisible to the end user,” Aasman stated. An estimable example of this use case involves P2P networks, in which “people just use peer-to-peer databases that record transactions,” Aasman mentioned. “But these peer-to-peer transactions are checked by the blockchain to make sure people aren’t cheating.” Blockchain is used to manage transactions between parties in supply chains in much the same way. Blockchain aids organizations with this P2P customer use case because without it, “it’s very, very complicated for normal people to get it done,” Aasman said about traditional approaches to inter-organization ledger systems. With each party operating on a single blockchain, however, transactions become indisputable once they are sanctioned between the participants.

Internal Governance and Security
Perhaps the most distinguishable feature of the foregoing use case is the fact that in most instances, end users won’t even know they’re working with blockchain. What Aasman called an “invisible” characteristic of the blockchain ledger system is ideal for internal use to monitor employees in accordance with data governance and security procedures. Although blockchain supports internal intelligence or compliance for security and governance purposes, it’s most applicable to external transactions between organizations. In finance—just like in supply chain or in certain insurance transactions—“you could have multiple institutions that do financial transactions between each other, and each of them will have a version of that database,” Aasman explained. Those using these databases won’t necessarily realize they’re fortified by blockchain, and will simply use them as they would any other transactional system. In this case, “an accountant, a bookkeeper or a person that pays the bills won’t even know there’s a blockchain,” commented Aasman. “He will just send money or receive money, but in the background there’s blockchain making sure that no one can fool with the transactions.”

Master Data Management
Despite the fact that individual end users may be ignorant of the deployment of blockchain in the above use cases, it’s necessary for underlying IT systems to be fully aware of which clusters are part of this ledger system. According to Aasman, users will remain unaware of blockchain’s involvement “unless, of course, someone was trying to steal money, or trying to delete intermediate transactions, or deny that he sent money, or sent the same money twice. Then the system will say hey, user X has engaged in a ‘confusing’ activity.” In doing so, the system will help preserve adherence to company policies related to security or data governance issues.

Since organizations will likely employ other IT systems without blockchain, Master Data Management hubs will be important for “deciding for which transactions this applies,” Aasman said. “It’s going to be a feature of MDM.” Mastering the data from blockchain transactions with centralized MDM approaches can help align this data with others vital to a particular business domain, such as customer interactions. Aasman revealed that “the people that make master data management have to specify for which table this actually is true. Not the end users: the architects, the database people, the DBAs.” Implementing the MDM schema for which to optimize such internal applications of blockchain alongside those for additional databases and sources can quickly become complex with traditional methods, and may be simplified via smart data approaches.

Overall Value
The rapidity of blockchain’s rise will ultimately be determined by the utility the enterprise can derive from its technologies, as opposed to simply limiting its value to financial services and cryptocurrency. There are just as many telling examples of applying blockchain’s immutability to various facets of government and healthcare, or leveraging smart contracts to simplify interactions between business parties. By using this technology to better customer relations, reinforce data governance and security, and assist specific domains of MDM, organizations get a plethora of benefits from incorporating blockchain into their daily operations. The business value reaped in each of these areas could contribute to the overall adoption of this technology in both professional and private spheres of life. Moreover, it could help normalize blockchain as a commonplace technology for the contemporary enterprise.

Source: The Mainstream Adoption of Blockchain: Internal and External Enterprise Applications

When your organization decides to pull the trigger on a cloud migration, a lot of stuff will start happening all at once. Regardless of how long the planning process has been, once data starts being relocated, a variety of competing factors that have all been theoretical become devastatingly real: frontline business users still want to be able to run analyses while the migration is happening, your data engineers are concerned with the switch from whatever database you were using before, and the development org has its own data needs. With a comprehensive, BI-focused data strategy, you and your stakeholders will know what your ideal data model should look like once all your data is moved over. This way, as you’re managing the process and trying to keep everyone happy, you end in a stronger place when your migration is over than you were at the start, and isn’t that the goal?

BI-Focus and Your Data Infrastructure

“What does all this have to do with my data model?” you might be wondering. “And for that matter, my BI solution?”

I’m glad you asked, internet stranger. The answer is everything. Your data infrastructure underpins your data model and powers all of your business-critical IT systems. The form it takes can have immense ramifications for your organization, your product, and the new things you want to do with it (and how you want to build and expand on it and your feature offerings). Your data infrastructure is hooked into your BI solution via connectors, so it’ll work no matter where the data is stored. Picking the right data model, once all your data is in its new home, is the final piece that will allow you to get the most out of it with your BI solution. If you don’t have a BI solution, the perfect time to implement is once all your data is moved over and your model is built. This should all be part of your organization’s holistic cloud strategy, with buy-in from major partners who are handling the migration.

Picking the Right Database Model for You

So you’re giving your data a new home and maybe implementing a BI solution when it’s all done. Now, what database model is right for your company and your use case? There are a wide array of ways to organize data, depending on what you want to do with it.

One of the broadest is a conceptual model, which focuses on representing the objects that matter most to the business and the relationships between them (vs being a model of the data about those objects). This database model is designed principally for business users. Compare this to a physical model, which is all about the structure of the data. In this model, you’ll be dealing with tables, columns, relationships, and foreign keys, which distinguish the connections between the tables.

Now, let’s say you’re only focused on representing your data organization and architecture graphically, putting aside the physical usage or database management framework. In cases like these, a logical model could be the way to go. Examples of these types of databases include relational (dealing with data as tables or relations), network (putting data in the form of records), and hierarchical (which is a progressive tree-type structure, with each branch of the tree showing related records). These models all feature a high degree of standardization and cover all entities in the dataset and the relationships between them.

Got a wide array of different objects and types of data to deal with? Consider an object-oriented database model, sometimes called a “hybrid model.” These models look at their contained data as a collection of reusable software pieces, all with related features. They also consolidate tables but aren’t limited to the tables, giving you freedom when dealing with lots of varied data. You can use this kind of model for multimedia items you can’t put in a relational database or to create a hypertext database to connect to another object and sort out divergent information.

Lastly, we can’t help but mention the star schema here, which has elements arranged around a central core and looks like an asterisk. This model is great for querying informational indexes as part of a larger data pool. It’s used to dig up insights for business users, OLAP cubes, analytics apps, and ad-hoc analyses. It’s a simple, yet powerful, structure that sees a lot of usage, despite its simplicity.

Now What?

Whether you’re building awesome analytics into your app or empowering in-house users to get more out of your data, knowing what you’re doing with your data is key to maintaining the right models. Once you’ve picked your database, it’s time to pick your data model, with an eye towards what you want to do with it once it’s hooked into your BI solution.

Worried about losing customers? (Who isn’t?) A predictive churn model can help you get ahead of the curve by putting time and attention into relationships that are at risk of going sour. On the other side of the coin, predictive up- and cross-sell models can show you where you can get more money out of a customer and which ones are ripe to deepen your financial relationship.

What about your marketing efforts? A customer segmentation data model can help you understand the buying behaviors of your current customers and target groups and which marketing plays are having the desired effect. Or go beyond marketing with “next-best-action models” that take into account life events, purchasing behaviors, social media, and anything else you can get your hands on so that you can figure out what’s the next action with a given target (email, ads, phone call, etc.) to have the greatest impact. And predictive analyses aren’t just for humancentric activities—manufacturing and logistics companies can take advantage of maintenance models that can let you circumvent machine breakdowns based on historical data. Don’t get caught without a vital piece of equipment again.

Bringing It All Together with BI

Staying focused on your long-term goals is an important key to success. Whether you’re building a game-changing product or rebuilding your data model, having a firmly-defined goal makes all the difference when it comes to the success of your enterprise. If you’re already migrating your data to the cloud, then you’re at the perfect juncture to pick the right database and data models for your eventual use cases. Once these are set up, they’ll integrate seamlessly with your BI tool (and if you don’t have one yet, it’ll be the perfect time to implement one). Big moves like this represent big challenges, but also big opportunities to make lay the foundation for whatever you’re planning on building. Then you just have to build it!

Source: Cloud Migrations: Big Challenges, Big Opportunities by analyticsweek

The IBM “endorsement effect” has often shaped the computer industry over the years. In 1981, when IBM entered the personal computer business, the company decisively pushed an upstart technology into the mainstream.

In 2000, the open-source operating system Linux was viewed askance in many corporations as an oddball creation and even legally risky to use, since the open-source ethos prefers sharing ideas rather than owning them. But IBM endorsed Linux and poured money and people into accelerating the adoption of the open-source operating system.

On Monday, IBM is to announce a broadly similar move in big data software. The company is placing a large investment — contributing software developers, technology and education programs — behind an open-source project for real-time data analysis, called Apache Spark.

The commitment, according to Robert Picciano, senior vice president for IBM’s data analytics business, will amount to “hundreds of millions of dollars” a year.

In the big data software market, much of the attention and investment so far has been focused on Apache Hadoop and the companies distributing that open-source software, including Cloudera, Hortonworks and MapR. Hadoop, put simply, is the software that makes it possible to handle and analyze vast volumes of all kinds of data. The technology came out of the pure Internet companies like Google and Yahoo, and is increasingly being used by mainstream companies, which want to do similar big data analysis in their businesses.

But if Hadoop opens the door to probing vast volumes of data, Spark promises speed. Real-time processing is essential for many applications, from analyzing sensor data streaming from machines to sales transactions on online marketplaces. The Spark technology was developed at the Algorithms, Machines and People Lab at the University of California, Berkeley. A group from the Berkeley lab founded a company two years ago, Databricks, which offers Spark software as a cloud service.

Spark, Mr. Picciano said, is crucial technology that will make it possible to “really deliver on the promise of big data.” That promise, he said, is to quickly gain insights from data to save time and costs, and to spot opportunities in fields like sales and new product development.

IBM said it will put more than 3,500 of its developers and researchers to work on Spark-related projects. It will contribute machine-learning technology to the open-source project, and embed Spark in IBM’s data analysis and commerce software. IBM will also offer Spark as a service on its programming platform for cloud software development, Bluemix. The company will open a Spark technology center in San Francisco to pursue Spark-based innovations.

And IBM plans to partner with academic and private education organizations including UC Berkeley’s AMPLab, DataCamp, Galvanize and Big Data University to teach Spark to as many as 1 million data engineers and data scientists.

Ion Stoica, the chief executive of Databricks, who is a Berkeley computer scientist on leave from the university, called the IBM move “a great validation for Spark.” He had talked to IBM people in recent months and knew they planned to back Spark, but, he added, “the magnitude is impressive.”

With its Spark initiative, analysts said, IBM wants to lend a hand to an open-source project, woo developers and strengthen its position in the fast-evolving market for big data software.

By aligning itself with a popular open-source project, IBM, they said, hopes to attract more software engineers to use its big data software tools, too. “It’s first and foremost a play for the minds — and hearts — of developers,” said Dan Vesset, an analyst at IDC.

IBM is investing in its own future as much as it is contributing to Spark. IBM needs a technology ecosystem, where it is a player and has influence, even if it does not immediately profit from it. IBM mainly makes its living selling applications, often tailored to individual companies, which address challenges in their business like marketing, customer service, supply-chain management and developing new products and services.

“IBM makes its money higher up, building solutions for customers,” said Mike Gualtieri, a analyst for Forrester Research. “That’s ultimately why this makes sense for IBM.”

To read the original article on The New York Times, click here.

Source: IBM Invests to Help Open-Source Big Data Software — and Itself

IBM just released the results of a global study on how businesses can get the most value from Big Data and analytics. They found nine areas that are critical to creating value from analytics. You can download the entire study here.

IBM Institute for Business Value surveyed 900 IT and business executives from 70 countries from June through August 2013. The 50+ survey questions were designed to help translate concepts relating to generating value from analytics into actions.

Nine Levers to Value Creation

The researchers identified nine levers that help organizations create value from data. They compared leaders (those who identified their organization as substantially outperforming their industry peers) with the rest of the sample. They found that the leaders (19% of the sample) implement the nine levers to a greater degree than the non-leaders. These nine levers are:

1. Source of value: Actions and decisions that generate results. Leaders tend to focus primarily on their ability to increase revenue and less so on cost reduction.
2. Measurement: Evaluating the impact on business outcomes. Leaders ensure they know how their analytics impact business outcomes.
3. Platform: Integrated capabilities delivered by hardware and software. Sixty percent of Leaders have predictive analytic capabilities, as well as simulation (55%) and optimization (67%) capabilities.
4. Culture: Availability and use of data and analytics within an organization. Leaders make more than half of their decisions based on data and analytics.
5. Data: Structure and formality of the organization’s data governance process and the security of its data. Two-thirds of Leaders trust the quality of their data and analytics. A majority of leaders (57%) adopt enterprise-level standards, policies and practices to integrate data across the organization.
6. Trust: Organizational confidence. Leaders demonstrate a high degree of trust between individual employees (60% between executives, 53% between business and IT executives)
7. Sponsorship: Executive support and involvement. Leaders (56%) oversee the use of data and analytics within their own departments, guided by an enterprise-level strategy, common policies and metrics, and standardized methodologies compared to the rest (20%).
8. Funding: Financial rigor in the analytics funding process. Nearly two-thirds of Leaders pool resources to fund analytic investments. They evaluate these investments through pilot testing, cost/benefit analysis and forecasting KPIs.
9. Expertise: Development of and access to data management and analytic skills and capabilities. Leaders share advanced analytics subject matter experts across projects, where analytics employees have formalized roles, clearly defined career paths and experience investments to develop their skills.

The researchers state that each of the nine levers have a different impact on the organization’s ability to deliver value from the data and analytics; that is, all nine levers distinguish Leaders from the rest but each Lever impacts value creation in different ways. Enable levers need to be in place before value can be seen through the Drive and Amplify levers. The nine levers are organized into three levels:

1. Enable: These levers form the basis for big data and analytics.
2. Drive: These levers are needed to realize value from data and analytics; lack of sophistication within these levers will impede value creation.
3. Amplify: These levers boost value creation.

Recommendations: Creating an Analytic Blueprint

Next, the researchers offered a blueprint on how business leaders can translate the research findings into real changes for their own businesses. This operational blueprint consists of three areas: 1) Strategy, 2) Technology and 3) Organization.

1. Strategy

Strategy is about the deliberateness with which the organization approaches analytics. Businesses need to adopt practices around Sponsorship, Source of value and Funding to instill a sense of purpose to data and analytics that connects the strategic visions to the tactical activities.

2. Technology

Technology is about the enabling capabilities and resources an organization has available to manage, process, analyze, interpret and store data. Businesses need to adopt practices around Expertise, Data and Platform to create a foundation for analytic discovery to address today’s problems while planning for future data challenges.

3. Organization

Organization is about the actions taken to use data and analytics to create value. Businesses need to adopt practices around Culture, Measurement and Trust to enable the organization to be driven by fact-based decisions.

Summary

One way businesses are trying to outperform their competitors is through the use of analytics on their treasure trove of data. The IBM researchers were able to identify the necessary ingredients to extract value from analytics. The current research supports prior research on the benefits of analytics in business:

1. Top-performing businesses are twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.
2. Analytic innovators 1) use analytics primarily to increase value to the customer rather than to decrease costs/allocate resources, 2) aggregate/integrate different business data silos and look for relationships among once-disparate metric and 3) secure executive support around the use of analytics that encourage sharing of best practices and data-driven insights throughout their company.

Businesses, to extract value from analytics, need to focus on improving strategic, technological and organizational aspects on how they treat data and analytics. The research identified nine area or levers executives can use to improve the value they generate from their data.

For the interested reader, I recently provided a case study (see: The Total Customer Experience: How Oracle Builds their Business Around the Customer) that illustrates how one company uses analytical best practices to help improve the customer experience and increase customer loyalty.

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