Predictive Analytics, The game Changer BI

Dears,


Today we had a fantastic session on Predictive Analytic @ BI Conference Organized by Silicon India , Hyderabad chapter. I will post the discussion we had on a separate blog article and in this article I want to provide some precursor about this topic.

Why Predictive Analytics ?

Prediction stands for Classification and estimation of a value or behavior that is yet to occur.
Renowned business management guru Peter Drucker once said that what cannot be measured cannot be managed. In light of this theory, reporting, scorecarding, key performance indicators (KPIs), and other strategies emerged as management tools, helping to ensure that performance is consistent, and does not deviate from operational and profitability objectives. Perhaps the greatest benefit of those tools is that they give managers the ability to monitor processes after they have been executed, and react accordingly by fixing them when things go wrong.In addition to what happened yesterday or last week, managers are anxious to know what will happen tomorrow. For example, what will be the demand for a product or service? What revenue  can be expected and from what channels (direct sales, partners, etc.)? Which customers are likely
to buy a product? If accurate expectations can be formed about the future, managers can proactively structure workflows and allocate resources to maximize productivity and profits. The more precise the expectations, the greater the profits that can be realized – more efficiently and with fewer resources


Modern information systems collect tremendous volumes of data. A customer profile can include more than 200 descriptive variables, such as income, age group, gender, occupation, and education level. A crime incident may also have many attributes – weather conditions, location indicators, and victim characteristics. How can it be decided which factors matter and which ones don’t? And how does one determine the relative importance of each factor. For example, how much more important is income than education when performing credit application approvals? With the aid of predictive modeling technology, managers can do what the human mind alone cannot. They can assess which factors are relevant or assign weights to those factors. Predictive analytics eliminates guesswork, helping managers make better decisions.
Predictive Analytics can serve only as a guide to the future based on its understanding of the past. Changing circumstances, such as the entrance of a new competitor into the market, may disrupt the accuracy of these predictions. Second, data mining can only analyze the data it is presented with. Information about the customer's motivation or emotions is not typically captured in enterprise data systems, so data mining is better at analyzing what the customer did than at understanding why the customer may have done it

The purpose of any CRM activity is to turn an action the customer would have taken into one that delivers the most value to the enterprise. This may mean accelerating or bundling a planned purchase or delaying or dissuading a planned switch to a competitor. Most enterprise decisions about how to behave are based on what the customer has already done (collected from operational systems and stored in the data warehouse). The problem is that these actions are a lagging indicator of the customer relationship; they tell you what the customer has already done and leave the enterprise to extrapolate the implications: Why did the customer do it? Will the customer do it again? What else might the customer do? Although not a bad place to start,enterprises are increasingly looking to identify leading indicators about the state of the relationship. These leading indicators can be the words and thoughts of the customers, directly expressed with the intention that the enterprise should react to them. This idea of capturing the voice of the customer will grow more prevalent as technologies mature, and as best practices and business cases enter the public domain. Now is the time for forward-looking, analytical organizations to prepare for the coming questions and initiate pilot programs and proofs of concept to understand the real potential behind these questions..

What is the future of the predictive analytics market?

Interest in unstructured data is growing rapidly in today's social business environment, although the variety of unstructured data that exists for organizations to tap into is much more diverse. Unlike pattern discovery, which relies on technology (unless you are starring in "A Beautiful Mind," identifying patterns from numerical displays is almost impossible), content analytics seek to duplicate something that most people would agree is better-performed by people. The drawback to the manual approach is that it almost always fails in at least one respect. The sheer volume of information now available would defeat most organizations' ability to comprehensively analyze it all, forcing a potentially misleading reliance on sampling. In the event a large enough team can be assembled, the issue becomes whether enough consistency in the categorization can be created. Even simple distinctions between good and bad can be difficult to define ("Not as bad as I expected" is a very ambiguous statement, for example). Applying technology to the problem allows both of these pitfalls to be avoided, albeit at the risk of having a completely consistently inaccurate interpretation if the deployment is not correctly managed. Content comes in many forms and with many different characteristics. In most cases, a text-mining solution can be employed to analyze content, but this approach is not always the best. In particular, a decision between using speech analytics to analyze an actual conversation (for example, between a customer service agent and the customer) or using a text mining system to analyze the call enter agent's notes may result in very different analyses. In most cases, we would recommend using speech analytics to analyze the call recording.Analyzing Web content, particularly from social media sites, can also be performed using either text-mining applications or dedicated media-monitoring service providers. In general, we recommend the use of mediamonitoring service providers as the better long-term solution (text-mining applications are best-suited for tactical or ad hoc analysis).

Combining traditional data with the positioning information from mobile devices and the ability to append information to the identified location allows significant improvements in predictive power and the ability to deliver context relevant messages to customers.Incorporating location into predictive models can drive a variety of applications in multiple industries.


This is quiet a fascinating topic and I look forward to explore more and welcome you to share your views /predictions about predictive analytics.

Your Loving P&C
DC*

Mastering the Politics to make your “MDM and Data Governance program” a Success

Mastering the Politics to make your “MDM and Data Governance program”  a Success

The field of master data management (MDM) is developing quickly. MDM hubs from large technology vendors, such as Oracle, IBM and SAP, and smaller vendors, such as Siperian, Initiate Systems and D&B/Purisma, are maturing rapidly. Because MDM is growing so quickly, systems integrators have been piling into the space, repurposing their other methodologies and rebranding their resources. But getting the technology right - selecting the right software products and implementing them well - is only part of the challenge. MDM is a set of disciplines and processes for ensuring the accuracy, completeness, timeliness and consistency of the most important types (or domains) of enterprise data across different applications, systems and databases, multiple business processes, functional areas, organizations, geographies and channels. Whenever a business activity crosses so many boundaries within a large organization, there are going to be political issues. Experience shows that to be successful, your MDM strategy needs to take that into account and to embrace it.

Just as a good program management office (PMO) needs experienced project managers, a successful MDM initiative needs a politically savvy leader within the organization who can drive the project, keep senior management engaged and supportive, allow the business to “own” the initiative but keep IT involved as a supporter and facilitator, address the cultural issues and, most importantly, balance the need for quick wins and securing and keeping the funding while maintaining the longer-term vision and architectural integrity that will keep the MDM program from becoming just another island of data.

One big mistake to avoid is not to realize how political the whole MDM process really is. “Politics, n. Strife of interests masquerading as a contest of principles.”In other words, you’ll see the same jockeying for position, the same personality conflicts and the same turf wars in an MDM initiative that you’d see on any large project involving both business and IT. In fact, MDM projects can be worse, because they typically involve marketing, sales, finance, customer service and potentially other powerful groups within the company.

The very nature of MDM is responsible for the degree of its political difficulty. Take an important domain of master data that is critical to the success of the business, such as customers or products. Then go across all of the different business processes and functional areas that touch or own the data in that domain. Then go down into each and every important “silo” of data - applications, systems and databases - in that domain. Then throw in the implementation of new, potentially disruptive technology, the redesigning of a large number of business processes, and having to change the organization’s culture and attitude toward the importance of managing information as a corporate asset. It should be no surprise that an MDM initiative has the potential to be the most politically charged project your organization has seen in quite a while.

What To Do About It

Start by understanding the landscape and having a plan. Now that you realize how important the political dimension of MDM is going to be to your project, think it through. Map out the important constituencies involved in your project - sales, finance, operations, etc. If your company is large enough, make sure to include all of the important geographies where you operate.

Take the current organization chart (if you can find it or create it) and diagram the leaders of each area and their important direct reports. Indicate in your map their attitudes toward one another, both personally and politically. It doesn’t have to be very elaborate, but the goal is to avoid being surprised later by political issues or animosities you could have anticipated.

In planning for the political dimension of your MDM project, keep in mind that you’re going to have to deal with both the initial phase - securing the funding and then actually doing the implementation - and the ongoing phase. There are still many companies out there who think that when their MDM implementation goes live that the initiative is over. In reality, it’s just beginning.

Organizational Change Management

The need for data governance sets MDM apart. The continuing political framework takes ownership of the data, manages it proactively, resolves issues and disputes that arise over time and sets policies within the organization for information management, security and privacy, data quality, etc.

Two key disciplines to apply during the initial and ongoing phases are education and communication. Best practices here include electronic newsletters, internal portals, “lunch and learn” sessions and attending internal departmental meetings.

In many ways, these are the same tools and techniques used on large customer relationship management (CRM) and enterprise resource planning (ERP) initiatives - because they work! An organized approach to organizational change management can make the difference between a challenging but successful implementation and a morass of political wrangling and internecine warfare.

Become an internal evangelist for MDM in your organization. Have your 30-second elevator pitch down cold, and then develop a five-minute version of the strategy as well as a 15-minute, 30-minute and 60-minute version. Use every opportunity you get to educate people within the organization on what MDM is and what your company’s MDM strategy is, then use all of the communications tools at your disposal to keep people involved.

MDM can sound like plumbing to a lot of people, so go out of your way to find the business wins that you are generating - increased revenue, better alignment with strategy, cost reduction, improved customer service, more efficient compliance, etc. - and document, quantify and publicize them.

Although it’s more work to “do and publicize” than simply to “do,” down the road, you’ll be glad you did. Politically, it’s harder to argue with or cut the budget of a program that’s visibly solving problems for the enterprise, increasing revenue, etc.

One of the most successful MDM and data governance programs I’ve heard of documented every win they had, and over time it reached a total of $50 million. The leader of that program had built up quite a bit of political capital and “career durability insurance,” and the program’s funding was never threatened.

Importance of Data Governance

Now that you’re warmed up on the political dimension, start thinking about your data governance approach. Companies that have implemented MDM successfully give credit to a strong data governance program to accompany the implementation of an MDM hub and related technology.

While the names may differ, most companies create a three-layered structure for data governance: an executive-level data governance steering committee, a midlevel data governance council and a set of business and IT data stewards. The data stewards are represented on the council, and members of the council are also members of the steering committee.

While this structure may or may not fit your situation, it should get you thinking about how to get executive ownership and involvement at the steering committee level, who are the “champions” that will drive the data governance council and help to set policy, and where to find the business and IT data stewards that will carry out those policies and do the day-to-day work.

The steering committee is employed to resolve issues that cannot be settled by the data governance council. It will probably meet infrequently (quarterly or monthly), although it may meet more often at first. It will deal with foundation issues such as “who owns the data” and “who gets to make decisions about the data.”

The data governance council will typically be led by someone who is responsible for aligning the steering committee’s projects between the business and IT. That person will manage and oversee the data stewards and work with the data owners to implement data governance policies. There may be several different types of data stewards - business and IT, as well as corporate and line of business.

One major political challenge is that you will almost never be given a mandate to go out and hire new people. You’ll have to identify resources in place, people who are already doing data stewardship, although perhaps in a limited way or in a specific application or functional area. Look for the qualities you need in existing personnel, and plan for redeploying those existing people (at least partially) into the new data governance organization you’ll be creating.

Strategic Alignment

Another critical success factor is the degree to which you can line up your entire MDM and data governance program behind the company’s already-announced strategic priorities. Work with the senior executives you have access to and take the time to understand the company’s top-five strategic drivers. Find a way to tell how your MDM vision directly supports your overreaching corporate objectives, whether they’re “grow market share,” “improve productivity,” “comply with Basel II,” “revenue enhancement” or “support mergers and acquisitions.”

If you can do that - if you can work backward from those objectives to your program in an unbroken line, you should be able to find the political backing and funding for your project. If your project is a solution in search of a problem, you could be in for a hard time. I’ve seen initiatives at major companies where the IT folks went off and implemented a customer hub without involving the business and without tying the hub project back to the company’s strategic objectives. Even when you succeed in getting your initial funding, the implementation is going to be rocky, and getting funding in subsequent years can be difficult.

Mastering the Political Aspects

The rewards for “going over to the dark side” - delving into and mastering the political aspects of a complex business and IT project - are many. It may be against your instincts. You don’t have to become a slick corporate political animal. But knowing your company’s strategic objectives, making sure your MDM vision and strategy aligns with them, mapping the political landscape and planning for how to handle it, using organizational change management techniques and including a data governance structure from the beginning should make your life and your MDM initiative smoother and more successful.

Loving P&C

DC*

The Chicken & Egg Syndrome : Data Governance is a Big Challenge

Dears,

The maturity of seeing MDM as a strategic project and not as IT Project is one of the lessons learned by big enterprises after they suffered heavy losses due to poor data quality and data governance initiatives but still today there is a huge debate about what they want to achieve with data governance program.

Data governance suffers a bit from the "chicken or the egg “syndrome. People at your company aren't going to understand what data governance is and what it can do for them until they actually see the results. However, getting the initiative funded and launched will only happen if you can convince your company of the tangible benefits of data governance. That can be difficult when there's no actual program in place. Selling people on the benefits of data governance (a program that's going to cost money) calls for some persuasive salesmanship and well-crafted internal marketing. If your company has suffered from a horror story that could have been averted with data governance, convincing senior management of the need for data governance won't be too difficult. Tell the horror story a few times, embellish a bit where needed, and funding should be easily agreed upon.

An obstacle to governance can arise if your company has already tried to establish a program and failed. Perhaps the business case wasn't strong enough, management wasn't convinced, and everyone was sent back to the drawing board for another try. Regardless, making the pitch for data governance has to come from an unmet need to confront the results of poor business practices. One way to approach it is to research the avoidance cost: "How much is it costing us to not have data governance in place today?"

This requires spending some time away from your desk. Start by visiting business owners and gathering their data-related frustrations and pain points. Ask them for anecdotal evidence of returned mailings, orders shipped to wrong places, unhappy customers, fees and charges from third-party logistics companies, overstocked inventory due to duplicate part numbers, regulatory compliance issues and fines, etc. Once you get business owners talking about this, either individually or in small groups, you'll likely uncover a surge of unmet needs.

Be prepared for the question "What are you going to do about it?" Explain that you're gathering information to build a business case for a data governance function within the enterprise, and that every story helps. Wherever you can, put a dollar amount next to the pain point. Even if it's an estimate, this evidence will support your hard-dollar answer to the question "How much is it costing us to not have data governance today?"Based on the information you gather, use a simple survey tool to extend your reach. Structure your questions thoughtfully to get at people's overall business goals and how data governance can help support them. Make sure there are enough free-form answers in the survey so people can voice their ideas or frustrations with ungoverned, siloed information throughout your enterprise.

Ultimately, data governance will not be established and primed to flourish without the consent of the business and process owners with real power in the company. You need to recruit them from the  beginning to back the establishment and fund the data governance function. If they don't see the benefit, or if they're actively against it for some reason, a data governance program will never get off the ground. That makes it essential to define a clear mandate of what data governance will do within your organization, making sure that the vision contains enough benefits so even the skeptics agree it's worth doing.

You'll have to live up to that promise. The initial data governance framework needs to be fleshed out and made a reality. Sometimes, that can be just as challenging as getting the approvals and funding. Hiring a few critical people, forming a data governance council and getting the right people to participate, gaining a few early wins, resisting the temptation to make everything a data governance problem - these are some of the issues you'll deal with in the early stages of your program. Try to imagine how you'll confront these questions ahead of time. Nothing is insurmountable, but be prepared for growing pains as your program gets off the ground and begins to stretch its political legs.

Ultimately, data governance is a political challenge. It needs to be led by a person with a lot of business savvy and political skill. Budgets, headcounts, business processes and organizational alignment will be more important than technology selection and implementation. But in the end, there's not much point in trying to manage your data on an enterprise-wide basis if you're not going to govern it that way too.

The technology is the easy part. Understanding what drives people - individuals, societies, what makes cultures clash - all of those questions are way, way harder to answer than how to solve any particular technical problem.

Watch this space for the next article on this subject

Loving P&C

DC*