Category Archives: Master Data Management
Step 1: Determine if you have a customer data problem
A statement I often hear from marketing and sales leaders unfamiliar with the concept of mastering customer data is, “My CRM application is our single source of trusted customer data.” They use CRM to onboard new customers, collecting addresses, phone numbers and email addresses. They append a DUNS number. So it’s no surprise they may expect they can master their customer data in CRM. (To learn more about the basics of managing trusted customer data, read this: How much does bad data cost your business?)
It may seem logical to expect your CRM investment to be your customer master – especially since so many CRM vendors promise a “360 degree view of your customer.” But you should only consider your CRM system as the source of truth for trusted customer data if:
· You have only a single instance of Salesforce.com, Siebel CRM, or other CRM
· You have only one sales organization (vs. distributed across regions and LOBs)
· Your CRM manages all customer-focused processes and interactions (marketing, service, support, order management, self-service, etc)
· The master customer data in your CRM is clean, complete, fresh, and free of duplicates
Unfortunately most mid-to-large companies cannot claim such simple operations. For most large enterprises, CRM never delivered on that promise of a trusted 360-degree customer view. That’s what prompted Gartner analysts Bill O’Kane and Kimbery Collins to write this report, MDM is Critical to CRM Optimization, in February 2014.
“The reality is that the vast majority of the Fortune 2000 companies we talk to are complex,” says Christopher Dwight, who leads a team of master data management (MDM) and product information management (PIM) sales specialists for Informatica. Christopher and team spend each day working with retailers, distributors and CPG companies to help them get more value from their customer, product and supplier data. “Business-critical customer data doesn’t live in one place. There’s no clear and simple source. Functional organizations, processes, and systems landscapes are much more complicated. Typically they have multiple selling organizations across business units or regions.”
As an example, listed below are typical functional organizations, and common customer master data-dependent applications they rely upon, to support the lead-to-cash process within a typical enterprise:
· Marketing: marketing automation, campaign management and customer analytics systems.
· Ecommerce: e-commerce storefront and commerce applications.
· Sales: sales force automation, quote management,
· Fulfillment: ERP, shipping and logistics systems.
· Finance: order management and billing systems.
· Customer Service: CRM, IVR and case management systems.
The fragmentation of critical customer data across multiple organizations and applications is further exacerbated by the explosive adoption of Cloud applications such as Salesforce.com and Marketo. Merger and acquisition (M&A) activity is common among many larger organizations where additional legacy customer applications must be onboarded and reconciled. Suddenly your customer data challenge grows exponentially.
Step 2: Measure how customer data fragmentation impacts your business
Ask yourself: if your customer data is inaccurate, inconstant and disconnected can you:
· See the full picture of a customer’s relationship with the business across business units, product lines, channels and regions?
· Better understand and segment customers for personalized offers, improving lead conversion rates and boosting cross-sell and up-sell success?
· Deliver an exceptional, differentiated customer experience?
· Leverage rich sources of 3rd party data as well as big data such as social, mobile, sensors, etc.., to enrich customer insights?
“One company I recently spoke with was having a hard time creating a single consolidated invoice for each customer that included all the services purchased across business units,” says Dwight. “When they investigated, they were shocked to find that 80% of their consolidated invoices contained errors! The root cause was innaccurate, inconsistent and inconsistent customer data. This was a serious business problem costing the company a lot of money.”
Let’s do a quick test right now. Are any of these companies your customers: GE, Coke, Exxon, AT&T or HP? Do you know the legal company names for any of these organizations? Most people don’t. I’m willing to bet there are at least a handful of variations of these company names such as Coke, Coca-Cola, The Coca Cola Company, etc in your CRM application. Chances are there are dozens of variations in the numerous applications where business-critical customer data lives and these customer profiles are tied to transactions. That’s hard to clean up. You can’t just merge records because you need to maintain the transaction history and audit history. So you can’t clean up the customer data in this system and merge the duplicates.
The same holds true for B2C customers. In fact, I’m a nightmare for a large marketing organization. I get multiple offers and statements addressed to different versions of my name: Jakki Geiger, Jacqueline Geiger, Jackie Geiger and J. Geiger. But my personal favorite is when I get an offer from a company I do business with addressed to “Resident”. Why don’t they know I live here? They certainly know where to find me when they bill me!
Step 3: Transform how you view, manage and share customer data
Why do so many businesses that try to master customer data in CRM fail? Let’s be frank. CRM systems such as Salesforce.com and Siebel CRM were purpose built to support a specific set of business processes, and for the most part they do a great job. But they were never built with a focus on mastering customer data for the business beyond the scope of their own processes.
But perhaps you disagree with everything discussed so far. Or you’re a risk-taker and want to take on the challenge of bringing all master customer data that exists across the business into your CRM app. Be warned, you’ll likely encounter four major problems:
1) Your master customer data in each system has a different data model with different standards and requirements for capture and maintenance. Good luck reconciling them!
2) To be successful, your customer data must be clean and consistent across all your systems, which is rarely the case.
3) Even if you use DUNS numbers, some systems use the global DUNS number; others use a regional DUNS number. Some manage customer data at the legal entity level, others at the site level. How do you connect those?
4) If there are duplicate customer profiles in CRM tied to transactions, you can’t just merge the profiles because you need to maintain the transactional integrity and audit history. In this case, you’re dead on arrival.
There is a better way! Customer-centric, data-driven companies recognize these obstacles and they don’t rely on CRM as the single source of trusted customer data. Instead, they are transforming how they view, manage and share master customer data across the critical applications their businesses rely upon. They embrace master data management (MDM) best practices and technologies to reconcile, merge, share and govern business-critical customer data.
More and more B2B and B2C companies are investing in MDM capabilities to manage customer households and multiple views of customer account hierarchies (e.g. a legal view can be shared with finance, a sales territory view can be shared with sales, or an industry view can be shared with a business unit).
According to Gartner analysts Bill O’Kane and Kimberly Collins, “Through 2017, CRM leaders who avoid MDM will derive erroneous results that annoy customers, resulting in a 25% reduction in potential revenue gains,” according to this Gartner report, MDM is Critical to CRM Optimization, February 2014.
Are you ready to reassess your assumptions about mastering customer data in CRM?
Get the Gartner report now: MDM is Critical to CRM Optimization.
Did you know the 2014 Brasil World Cup is actually the World Cup of Data? In addition to the visible matches played on the pitch, eShops will be in a simultaneous struggle to win real-time online merchandise customers.
Let me explain. Jogi Löw, the manager of the German team, is known for his stylish attire. At every major event, each European Cup and World Cup, he wears newly designed shirts and suits. As a result, when television audiences see each new article of clothing, there is a corresponding increase in related online retail activity. When Löw began this tradition, people didn’t know that his outfits were made by Strenesse. As a result, people searched using the keywords “Jogi Löw Shirt.” This drove traffic to the eShop with the best search engine optimization, giving them more conversions and more revenue.
If a manager’s attire drives online retail sales, imagine how much demand there is for the jerseys worn by the most visible World Cup athletes? Many of the these players have huge social media followings. Consider the size of the social media followings of Ronaldo, Kakà, Neymar, Ronaldinho and Wayne Rooney:
There is huge demand for these player’s jerseys. This demand will only increase as the games progress. Once the winner is decided, Google searches will rise for phrases like “World Cup Winner Jersey 2014 of xxx”. Some refer to this as the super long tail. And research does show that search queries with 3 or more words have better conversion rates than queries with only 1 or 2 words.
Who can predict the winners?
What happens if a fairly unknown player scores the last goal in over time? How will that event impact social media activity and search engine volumes? Who will be able to leverage this activity to sell the relevant merchandising products fast enough? The eShop with the best data will have the quickest response. And the eShop with the quickest response will get the traffic and the revenue.
The world cup is a battle. The early bird closes the sale. It’s time to play the World Cup of Data.
In my marketing classes, I like to share on the works of Michael Porter’s Competitive Strategy. This includes discussing his three generic business strategies. We discuss, for example, the difference between an “efficiency strategy” (aka Walmart) and an “effectiveness strategy” (aka Target or even better, a high end service oriented retailer). I always make sure that students include in their thinking on differentiation the impact of customer service.
One of these high end service oriented retailers is using technology to increase its customer intimacy as well as holistic customer knowledge. Driving this for them involves understanding when customers use their full price and off price customer purchase channels. I was so fascinated about their question that I decided to ask the font of all wisdom, my wife. She said that her choice of channel is based on my current salary or her projected length of use of an item. So if she is buying a jacket that she wants to use for years, she will go to the full price channel but for a dress or pair of shoes for one time use like a Wedding, she will go to the lower priced channel. Clearly, there is more than one answer to these questions. This retailer wants to understand the answers by customer segments.
To create an understanding of each customer segment, this retailer wants to create a “high fidelity” view of data coming from customers, markets, and transactional interactions. This means that that they need two new business capabilities. First is a single integrated view of their customers across channels and the ability to see the cause and effect of customer channel selection decisions. Do customers spend more time at the full price channel option when, for example, sale offerings are going on?
To solve these problems, the retailer has implemented two technology approaches, master data management to bring together its disparate views of customer and big data for quick hypothesis testing of customer data from structured and unstructured sources. With Master Data, they get a single view of customer across differing IT systems. For separate customer specific analysis they have created operational and analytic views on top of the MDM system. And while they have an enterprise data warehouse and multiple analytical data marts, they have also created a HADOOP cluster to test hypothesis about the cross channel customer segments. They are using the single view of customer regardless of channels and transaction history to understand when customers use which channel and as well what marketing or other campaigns pulled the customer in. With this, they are creating inferred attributes for customer market segments.
Clearly, the smarter the retailer gets, the greater the differentiation the retailer services can be to customers. At the same time, the data let’s the retailer optimize marketing between channels. This is using data to create service differentiation.
Before I joined Informatica I worked for a health plan in Boston. I managed several programs including CMS Five Start Quality Rating System and Risk Adjustment Redesign. We recognized the need for a robust diagnostic profile of our members in support of risk adjustment. However, because the information resides in multiple sources, gathering and connecting the data presented many challenges. I see the opportunity for health plans to transform risk adjustment.
As risk adjustment becomes an integral component in healthcare, I encourage health plans to create a core competency around the development of diagnostic profiles. This should be the case for health plans and ACO’s. This profile is the source of reimbursement for an individual. This profile is also the basis for clinical care management. Augmented with social and demographic data, the profile can create a roadmap for successfully engaging each member.
Why is risk adjustment important?
Risk Adjustment is increasingly entrenched in the healthcare ecosystem. Originating in Medicare Advantage, it is now applicable to other areas. Risk adjustment is mission critical to protect financial viability and identify a clinical baseline for members.
What are a few examples of the increasing importance of risk adjustment?
1) Centers for Medicare and Medicaid (CMS) continues to increase the focus on Risk Adjustment. They are evaluating the value provided to the Federal government and beneficiaries. CMS has questioned the efficacy of home assessments and challenged health plans to provide a value statement beyond the harvesting of diagnoses codes which result solely in revenue enhancement. Illustrating additional value has been a challenge. Integrating data across the health plan will help address this challenge and derive value.
2) Marketplace members will also require risk adjustment calculations. After the first three years, the three “R’s” will dwindle down to one ‘R”. When Reinsurance and Risk Corridors end, we will be left with Risk Adjustment. To succeed with this new population, health plans need a clear strategy to obtain, analyze and process data. CMS processing delays make risk adjustment even more difficult. A Health Plan’s ability to manage this information will be critical to success.
3) Dual Eligibles, Medicaid members and ACO’s also rely on risk management for profitability and improved quality.
With an enhanced diagnostic profile — one that is accurate, complete and shared — I believe it is possible to enhance care, deliver appropriate reimbursements and provide coordinated care.
How can payers better enable risk adjustment?
- Facilitate timely analysis of accurate data from a variety of sources, in any format.
- Integrate and reconcile data from initial receipt through adjudication and submission.
- Deliver clean and normalized data to business users.
- Provide an aggregated view of master data about members, providers and the relationships between them to reveal insights and enable a differentiated level of service.
- Apply natural language processing to capture insights otherwise trapped in text based notes.
With clean, safe and connected data, health plans can profile members and identify undocumented diagnoses. With this data, health plans will also be able to create reports identifying providers who would benefit from additional training and support (about coding accuracy and completeness).
What will clean, safe and connected data allow?
- Allow risk adjustment to become a core competency and source of differentiation. Revenue impacts are expanding to lines of business representing larger and increasingly complex populations.
- Educate, motivate and engage providers with accurate reporting. Obtaining and acting on diagnostic data is best done when the member/patient is meeting with the caregiver. Clear and trusted feedback to physicians will contribute to a strong partnership.
- Improve patient care, reduce medical cost, increase quality ratings and engage members.
As I browsed my BBC app a few weeks ago, I ran into this article about environmental contamination of oil wells in the UK, which were left to their own devices. The article explains that a lack of data and proper data management is causing major issues for gas and oil companies. In fact, researchers found no data for more than 2,000 inactive wells, many of which have been abandoned or “orphaned”(sealed and covered up). I started to scratch my head imagining what this problem looks like in places like Brazil, Nigeria, Malaysia, Angola and the Middle East. In these countries and regions, regulatory oversight is, on average, a bit less regulated.
On top of that, please excuse my cynicism here, but an “Orphan” well is just as ridiculous a concept as a “Dry” well. A hole without liquid inside is not a well but – you guessed it – a hole. Also, every well has a “Parent”, meaning
- The person or company who drilled it
- A land owner who will get paid from its production and allowed the operation (otherwise it would be illegal)
- A financier who fronted the equipment and research cost
- A regulator, who is charged with overseeing the reservoir’s exploration
Let the “hydrocarbon family court judge” decide whose problem this orphan is with well founded information- no pun intended. After all, this “domestic disturbance” is typically just as well documented as any police “house call”, when you hear screams from next door. Similarly, one would expect that when (exploratory) wells are abandoned and improperly capped or completed, there is a long track record about financial or operational troubles at the involved parties. Apparently I was wrong. Nobody seems to have a record of where the well actually was on the surface, let alone subsurface, to determine perforation risks in itself or from an actively managed bore nearby.
This reminds me of a meeting with an Asian NOC’s PMU IT staff, who vigorously disagreed with every other department on the reality on the ground versus at group level. The PMU folks insisted on having fixed all wells’ key attributes:
- Knowing how many wells and bores they had across the globe and all types of commercial models including joint ventures
- Where they were and are today
- What their technical characteristics were and currently are
The other departments, from finance to strategy, clearly indicated that 10,000 wells across the globe currently being “mastered” with (at least initially) cheap internal band aid fixes has a margin of error of up to 10%. So much for long term TCO. After reading this BBC article, this internal disagreement made even more sense.
If this chasm does not make a case for proper mastering of key operational entities, like wells, I don’t know what does. It also begs the question how any operation with potentially very negative long term effects can have no legally culpable party being capture in some sort of, dare I say, master register. Isn’t this the sign of “rule of law” governing an advanced nation, e.g. having a land register, building permits, wills, etc.?
I rest my case, your honor. May the garden ferries forgive us for spoiling their perfectly manicured lawn. With more fracking and public scrutiny on the horizon, maybe regulators need to establish their own “trusted” well master file, rather than rely on oil firms’ data dumps. After all, the next downhole location may be just a foot away from perforating one of these “orphans” setting your kitchen sink faucet on fire.
Do you think another push for local government to establish “well registries” like they did ten years ago for national IDs, is in order?
Disclaimer: Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations and benchmarks. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warranty or representation of success, either express or implied, is made.
According to a recent article in the LA Times, healthcare costs in the United States far exceed costs in other countries. For example, heart bypass surgery costs an average of $75,345 in the U.S. compared to $15,742 in the Netherlands and $16,492 in Argentina. In the U.S. healthcare accounts for 18% of the U.S. GDP and is increasing.
Michelle Blackmer is an healthcare industry expert at Informatica. In this interview, she explains why business as usual isn’t good enough anymore. Healthcare organizations are rethinking how they do business in an effort to improve outcomes, reduce costs, and comply with regulatory pressures such as the Affordable Care Act (ACA). Michelle believes a data-driven healthcare culture is foundational to personalized medicine and discusses the importance of clean, safe and connected data in executing a successful transformation.
Q. How is the healthcare industry responding to the rising costs of healthcare?
In response to the rising costs of healthcare, regulatory pressures (i.e. Affordable Care Act (ACA)), and the need to better patient outcomes at lower costs, the U.S. healthcare industry is transforming from a volume-based to a value-based model. In this new model, healthcare organizations need to invest in delivering personalized medicine.
To appreciate the potential of personalized medicine, think about your own healthcare experience. It’s typically reactive. You get sick, you go to the doctor, the doctor issues a prescription and you wait a couple of days to see if that drug works. If it doesn’t, you call the doctor and she tries another drug. This process is tedious, painful and costly.
Now imagine if you had a chronic disease like depression or cancer. On average, any given prescription drug only works for half of those who take it. Among cancer patients, the rate of ineffectiveness jumps to 75 percent. Anti-depressants are effective in only 62 percent of those who take them.
Organizations like MD Anderson and UPMC aim to put an end to cancer. They are combining scientific research with access to clean, safe and connected data (data of all types including genomic data). The insights revealed will empower personalized chemotherapies. Personalized medicine offers customized treatments based on patient history and best practices. Personalized medicine will transform healthcare delivery. Click on the links to watch videos about their transformational work.
Q. What role does data play in enabling personalized medicine?
Data is foundational to value-based care and personalized medicine. Not just any data will do. It needs to be clean, safe and connected data. It needs to be delivered rapidly across hallways and across networks.
As an industry, healthcare is at a stage where meaningful electronic data is being generated. Now you need to ensure that the data is accessible and trustworthy so that it can be rapidly analyzed. As data is aggregated across the ecosystem, married with financial and genomic data, data quality issues become more obvious. It’s vital that you can define the data issues so the people can spend their time analyzing the data to gain insights instead of wading through and manually resolving data quality issues.
The ability to trust data will differentiate leaders from the followers. Leaders will advance personalized medicine because they rely on clean, safe and connected data to:
1) Practice analytics as a core competency
2) Define evidence, deliver best practice care and personalize medicine
3) Engage patients and collaborate to foster strong, actionable relationships
Take a look at this Healthcare eBook for more on this topic: Potential Unlocked: Transforming Healthcare by Putting Information to Work.
Q. What is holding healthcare organizations back from managing their healthcare data like other mission-critical assets?
When you say other mission-critical assets, I think of facilitates, equipment, etc. Each of these assets has people and money assigned to manage and maintain them. The healthcare organizations I talk to who are highly invested in personalized medicine recognize that data is mission-critical. They are investing in the people, processes and technology needed to ensure data is clean, safe and connected. The technology includes data integration, data quality and master data management (MDM).
What’s holding other healthcare organizations back is that while they realize they need data governance, they wrongly believe they need to hire big teams of “data stewards” to be successful. In reality, you don’t need to hire a big team. Use the people you already have doing data governance. You may not have made this a formal part of their job description and they might not have data governance technologies yet, but they do have the skillset and they are already doing the work of a data steward.
So while a technology investment is required and you need people who can use the technology, start by formalizing the data stewardship work people are doing already as part of their current job. This way you have people who understand the data, taking an active role in the management of the data and they even get excited about it because their work is being recognized. IT takes on the role of enabling these people instead of having responsibility for all things data.
Q. Can you share examples of how immature information governance is a serious impediment to healthcare payers and providers?
Sure, without information governance, data is not harmonized across sources and so it is hard to make sense of it. This isn’t a problem when you are one business unit or one department, but when you want to get a comprehensive view or a view that incorporates external sources of information, this approach falls apart.
For example, let’s say the cardiology department in a healthcare organization implements a dashboard. The dashboard looks impressive. Then a group of physicians sees the dashboard, point out erroes and ask where the information (i.e. diagnosis or attending physician) came from. If you can’t answer these questions, trace the data back to its sources, or if you have data inconsistencies, the dashboard loses credibility. This is an example of how analytics fail to gain adoption and fail to foster innovation.
Q. Can you share examples of what data-driven healthcare organizations are doing differently?
Certainly, while many are just getting started on their journey to becoming data-driven, I’m seeing some inspiring examples, including:
- Implementing data governance for healthcare analytics. The program and data is owned by the business and enabled by IT and supported by technology such as data integration, data quality and MDM.
- Connecting information from across the entire healthcare ecosystem including 3rd party sources like payers, state agencies, and reference data like credit information from Equifax, firmographics from Dun & Bradstreet or NPI numbers from the national provider registry.
- Establishing consistent data definitions and parameters
- Thinking about the internet of things (IoT) and how to incorporate device data into analysis
- Engaging patients through non-traditional channels including loyalty programs and social media; tracking this information in a customer relationship management (CRM) system
- Fostering collaboration by understanding the relationships between patients, providers and the rest of the ecosystem
- Analyzing data to understand what is working and what is not working so that they can drive out unwanted variations in care
Q. What advice can you give healthcare provider and payer employees who want access to high quality healthcare data?
As with other organizational assets that deliver value—like buildings and equipment—data requires a foundational investment in people and systems to maximize return. In other words, institutions and individuals must start managing their mission-critical data with the same rigor they manage other mission-critical enterprise assets.
Q. Anything else you want to add?
Yes, I wanted to thank our 14 visionary customer executives at data-driven healthcare organizations such as MD Anderson, UPMC, Quest Diagnostics, Sutter Health, St. Joseph Health, Dallas Children’s Medical Center and Navinet for taking time out of their busy schedules to share their journeys toward becoming data-driven at Informatica World 2014. In our next post, I’ll share some highlights about how they are using data, how they are ensuring it is clean, safe and connected and a few data management best practices. InformaticaWorld attendees will be able to download presentations starting today! If you missed InformaticaWorld 2014, stay tuned for our upcoming webinars featuring many of these examples.
It is troublesome to me to repeatedly get into conversations with IT managers who want to fix data “for the sake of fixing it”. While this is presumably increasingly rare, due to my department’s role, we probably see a higher occurrence than the normal software vendor employee. Given that, please excuse the inflammatory title of this post.
Nevertheless, once the deal is done, we find increasingly fewer of these instances, yet still enough, as the average implementation consultant or developer cares about this aspect even less. A few months ago a petrochemical firm’s G&G IT team lead told me that he does not believe that data quality improvements can or should be measured. He also said, “if we need another application, we buy it. End of story.” Good for software vendors, I thought, but in most organizations $1M here or there do not lay around leisurely plus decision makers want to see the – dare I say it – ROI.
However, IT and business leaders should take note that a misalignment due to lack OR disregard of communication is a critical success factor. If the business does not get what it needs and wants AND it differs what Corporate IT is envisioning and working on – and this is what I am talking about here – it makes any IT investment a risky proposition.
Let me illustrate this with 4 recent examples I ran into:
1. Potential for flawed prioritization
A retail customer’s IT department apparently knew that fixing and enriching a customer loyalty record across the enterprise is a good and financially rewarding idea. They only wanted to understand what the less-risky functional implementation choices where. They indicated that if they wanted to learn what the factual financial impact of “fixing” certain records or attributes, they would just have to look into their enterprise data warehouse. This is where the logic falls apart as the warehouse would be just as unreliable as the “compromised” applications (POS, mktg, ERP) feeding it.
Even if they massaged the data before it hit the next EDW load, there is nothing inherently real-time about this as all OLTP are running processes of incorrect (no bidirectional linkage) and stale data (since the last load).
I would question if the business is now completely aligned with what IT is continuously correcting. After all, IT may go for the “easy or obvious” fixes via a weekly or monthly recurring data scrub exercise without truly knowing, which the “biggest bang for the buck” is or what the other affected business use cases are, they may not even be aware of yet. Imagine the productivity impact of all the roundtripping and delay in reporting this creates. This example also reminds me of a telco client, I encountered during my tenure at another tech firm, which fed their customer master from their EDW and now just found out that this pattern is doomed to fail due to data staleness and performance.
2. Fix IT issues and business benefits will trickle down
Client number two is a large North American construction Company. An architect built a business case for fixing a variety of data buckets in the organization (CRM, Brand Management, Partner Onboarding, Mobility Services, Quotation & Requisitions, BI & EPM).
Grand vision documents existed and linked to the case, which stated how data would get better (like a sick patient) but there was no mention of hard facts of how each of the use cases would deliver on this. After I gave him some major counseling what to look out and how to flesh it out – radio silence. Someone got scared of the math, I guess.
3. Now that we bought it, where do we start
The third culprit was a large petrochemical firm, which apparently sat on some excess funds and thought (rightfully so) it was a good idea to fix their well attributes. More power to them. However, the IT team is now in a dreadful position having to justify to their boss and ultimately the E&P division head why they prioritized this effort so highly and spent the money. Well, they had their heart in the right place but are a tad late. Still, I consider this better late than never.
4. A senior moment
The last example comes from a South American communications provider. They seemingly did everything right given the results they achieved to date. This gets to show that misalignment of IT and business does not necessarily wreak havoc – at least initially.
However, they are now in phase 3 of their roll out and reality caught up with them. A senior moment or lapse in judgment maybe? Whatever it was; once they fixed their CRM, network and billing application data, they had to start talking to the business and financial analysts as complaints and questions started to trickle in. Once again, better late than never.
So what is the take-away from these stories. Why wait until phase 3, why have to be forced to cram some justification after the purchase? You pick, which one works best for you to fix this age-old issue. But please heed Sohaib’s words of wisdom recently broadcast on CNN Money “IT is a mature sector post bubble…..now it needs to deliver the goods”. And here is an action item for you – check out the new way for the business user to prepare their own data (30 minutes into the video!). Agreed?
Master data management (MDM) has come a long way in the past decade or so. When I was supporting my company’s customer master implementation back in 2001, my management was thrilled to simply have a customer master that brought a bit of order to the chaos sharing customer data between our CRM and ERP applications and downstream into our marketing data warehouse.
Fast forward to 2014 and mastering customer data alone is often table stakes for leadership trying to transform their business from a product- or account-centric to a customer-centric organizations.
Here at Informatica, we’ve seen over 75% of our MDM customers in the past year purchase for multidomain use cases – meaning the scope of their initiative often spans mastering data such as Customers, Suppliers and Products as part of a coordinated effort. These organizations have built compelling business cases to demonstrate that mastering multiple domains – and the relationships among those domains – is necessary. Only a true 360 degree view of relationships among any data can provide the necessary insights to deliver on the desired operational efficiencies, optimized customer experiences, and growth objectives for their companies.
The progress we’ve all made in multidomain MDM is impressive, but it’s just scratching the surface of what’s possible. What happens when MDM meets Cloud, Social, the Internet of Things and other master data enrichment sources such as D&B and Acxiom? Dennis Moore, Informatica’s GM and SVP for MDM, envisions that a new “Internet of Master Data” will be formed that can include a massive new set of sensor and social data which it leverages to infer and recommend a new class of relationship insights. For example, in addition to sentiment and relationships from social networks, location data from mobile devices and sensors can now inform customer – and product – behaviors that span beyond direct transactions and interactions within your traditional business applications.
Those of you who have invested in building a foundation of clean, consistent and connected data have a huge advantage as the value of MDM grows exponentially with the exponential growth of data. You are well-positioned to take advantage of the deeper insights and potential innovations now possible by adding Cloud, Social, and Machine data to optimizing analytics and operations.
This week at Informatica World 2014 in Las Vegas, we kicked off with our fantastic MDM Day pre-conference event with over 500 attendees. During the event, we shared some early insights into our MDM 10 release planned for later this year which integrates the Informatica Vibe engine and incorporates other elements of the just unveiled Informatica Intelligent Data Platform vision to make it easier for customers to gain a 360 degree view of their most critical business entities, including customers, suppliers, products and assets.
We continue to be inspired by our awesome MDM customers and partners, and we’re excited to see what they can do to harness the power of the Internet of Master Data!