Category Archives: Business Impact / Benefits
Recently, I presented a Business Value Assessment to a client. The findings were based on a revenue-generating state government agency. Everyone at the presentation was stunned to find out how much money was left on the table by not basing their activities on transactions, which could be cleanly tied to the participating citizenry and a variety of channel partners. There was over $38 million in annual benefits left over, which included partially recovered lost revenue, cost avoidance and reduction. A revenue driven business model could have prevented this.
Given the total revenue volume, this may seem small. However, after factoring in the little technology effort required to “collect and connect” data from transactions, it is actually extremely high.
The real challenge for this organization will be the required policy transformation to turn the organization from “data-starved” to “data-intensive”. This would eliminate strategic decisions around new products, locations and customers relying on surveys that face sampling errors, biases, etc. Additionally, surveys are often delayed, making them practically ineffective in this real-time world we live in today.
Despite no applicable legal restrictions, the leadership’s main concern was that gathering more data would erode the public’s trust and positive image of the organization.
To be clear; by “more” data being collected by this type of government agency I mean literally 10% of what any commercial retail entity has gathered on all of us for decades. This is not the next NSA revelation as any conspiracy theorist may fear.
While I respect their culturally driven self-censorship without legal interferences, it raises their stakeholders’ (the state’s citizenry) concern over its performance. To be clear, there would be no additional revenue for the state’s programs without more citizen data. You may believe that they already know everything about you, including your income, property value, tax information, etc. However, inter-departmental sharing of criminally-non-relevant information is legally constrained.
Another interesting finding from this evaluation was that they had no sense of conversion rate from email and social media campaigns. Impressions from click-throughs as well as hard/soft bounces were more important than tracking who actually generated revenue.
This is a very market-driven organization compared to other agencies. It actually does try to measure itself like a commercial enterprise and attempts to change in order to generate additional revenue for state programs benefiting the citizenry. I can only imagine what non-revenue-generating agencies (local, state or federal) do in this respect. Is revenue-oriented thinking something the DoD, DoJ or Social Security should adhere to?
Think tanks and political pundits are now looking at the trade-off between bringing democracy to every backyard on our globe and its long-term, budget ramifications. The DoD is looking to reduce the active component to its lowest in decades given the U.S. federal debt level.
A recent article in HBR explains that cost cutting has never sustained an organization’s growth over a longer period of time, but new revenue sources did. Is your company or government agency only looking at cost and personnel productivity?
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.
Recently, we posted an initial discussion between Informatica’s CMO Marge Breya and CIO Eric Johnson, explaining how CIOs and CMOs can align and thrive. In the dialog below, Breya and Johnson provide additional detail on how their departments partner effectively.
Q: Pretty much everyone agrees that marketing has changed from an art to a science. How does that shift translate into how you work together day to day?
Eric: The different ways that marketers now have to get to the prospects and customers to grow their marketshare has exploded. It used to be a single marketing solution that was an after-thought, and bolted on to the CRM solution. Now, there are just so many ways that marketers have to consider how they market to people. It’s driven by things going on in the market, like how people interact with companies and the lifestyle changes people have made around mobile devices.
Marge: Just look at the sheer number of systems and sources of data we care about. If you want to understand upsell and cross-sell for customers you have to look at what’s happening in the ERP system, what’s happened from a bookings standpoint, whether the customer is a parent or child of another customer, how you think about data by region, by industry by job title. And there’s how you think about successful conversion of leads. Is it the way you’d predicted? What’s your most valuable content? Who’s your most valuable outlet or event? What’s your ROI? You can’t get that from any one single system. More and more, it’s all about conversion rates, about forecasting and theories about how the business is working from a model standpoint. And I haven’t even talked about social.
Q: With so many emerging technologies to look at, how do CMOs reconcile the need to quickly add new products, while CIOs reconcile the need for everything to work securely and well together?
Eric: There’s this yin and yang that’s starting to build between the CIO and the CMO as we both understand each other and the world we each live in, and therefore collaborate and partner more. But at the same time, there’s a tension between a CMO’s need to bring in solutions very quickly, and the CIO’s need to do some basic vetting of that technology. It’s a tension between speed vs. scale and liability to the company. It’s on a case-by-case basis, but as a CIO you don’t say “no.” You give options. You show CMOs the tradeoffs they’re going to make.
There are also risks that are easy to take and worth taking. They won’t cause any problems with the enterprise on a security or integration perspective, so let’s just try it. It may not work — and that’s OK.
Marge: There’s temptation across departments for the shiny new object. You’ll hear about a new technology, and you think this might solve our problems, or move the business faster. The tension even within the marketing department is: do we understand how and if it will impact the business process? And do we understand how that business process will have to change if the shiny new object comes on board?
Q: CMOs are getting data from potentially hundreds of sources, including partners, third parties, LinkedIn and Google. How do the two of you work together to determine a trustworthy data source? Do you talk about it?
Eric: The issue of trusting your data and making sure you’re doing your due diligence on it is incredibly important. Without doing that, you are running the risk of finding yourself in a very tricky situation from a legal perspective, and potentially a liability perspective. To do that, we have a lot of technology that helps us manage a lot data sources coming into a single source of truth.
On top of that, we are working with marketers who are much more savvy about technology and data. And that makes IT’s job easier — and our partnership better — because we are now talking the same language. Sometimes it’s even hard to tell where the line between the two groups actually sits. Some of the marketing people are as technical as the IT people, and some of the IT people are becoming pretty well-versed in marketing.
Q: How do you decide what technologies to buy?
Marge: A couple of weeks ago we went on a shopping trip, and spent the day at a venture capital firm looking at new companies. It was fun. He and I were brainstorming and questioning each other to see if each technology would be useful, and could we imagine how everything would go together. We first explored possibilities, and then we considered whether it was practical.
Eric: Ultimately, Marge owns the budget. But before the budgeting cycle we sit down to discuss what things she wants to work on, and whether she wants to swap technology out. I make sure Marge is getting what she needs from the technologies. There’s a reliance on the IT team to do some due diligence on the technical aspects of this technology: Does it work. Do we want to do business with these people? Is it going to scale? So each party has a role to play in evaluating whether it’s a good solution for the company. As a CIO you don’t say “no” unless there’s something really bad, and you hope you have a relationship with the CMO where you can say here are the tradeoffs you’re making. You say no one has an agenda here, but here are the risks you have to be ok taking. It’s not a “no.” It’s options.
Looking for a data integration expert? Join the club. As cloud computing and big data become more desirable within the Global 2000, an abundance of data integration talent is required to make both cloud and big data work properly.
The fact of the matter is that you can’t deploy a cloud-based system without some sort of data integration as part of the solution. Either from on-premise to cloud, cloud-to-cloud, or even intra-company use of private clouds, these projects need someone who knows what they are doing when it comes to data integration.
While many cloud projects were launched without a clear understanding of the role of data integration, most people understand it now. As companies become more familiar with the could, they learn that data integration is key to the solution. For this reason, it’s important for teams to have at least some data integration talent.
The same goes for big data projects. Massive amounts of data need to be loaded into massive databases. You can’t do these projects using ad-hoc technologies anymore. The team needs someone with integration knowledge, including what technologies to bring to the project.
Generally speaking, big data systems are built around data integration solutions. Similar to cloud, the use of data integration architectural expertise should be a core part of the project. I see big data projects succeed and fail, and the biggest cause of failure is the lack of data integration expertise.
The demand for data integration talent has exploded with the growth of both big data and cloud computing. A week does not go by that I’m not asked for the names of people who have data integration, cloud computing and big data systems skills. I know several people who fit that bill, however they all have jobs and recently got raises.
The scary thing is, if these jobs go unfilled by qualified personnel, project directors may hire individuals without the proper skills and experience. Or worse, they may not hire anyone at all. If they plod along without the expertise required, in a year they’ll wonder why the systems are not sharing data the way they should, resulting in a big failure.
So, what can organizations do? You can find or build the talent you need before starting important projects. Thus, now is the time to begin the planning process, including how to find and hire the right resources. This might even mean internal training, hiring mentors or outside consultants, or working with data integration technology providers. Do everything necessary to make sure you get data integration done right the first time.
Bad data is bad for business. Ovum Research reported that poor quality data is costing businesses at least 30% of revenues. Never before have business leaders across a broad range of roles recognized the importance of using high quality information to drive business success. Leaders in functions ranging from marketing and sales to risk management and compliance have invested in world-class applications, six sigma processes, and the most advanced predictive analytics. So why are you not seeing more return on that investment? Simply put, if your business-critical data is a mess, the rest doesn’t matter.
Not all business leaders know there’s a better way to manage their business-critical data. So, I asked Dennis Moore, the senior vice president and general manager of Informatica’s MDM business, who clocked hundreds of thousands of airline miles last year visiting business leaders around the world, to talk about the impact of using accurate, consistent and connected data and the value business leaders can gain through master data management (MDM).
Q. Why are business leaders focusing on business-critical data now?
A. Leaders have always cared about their business-critical data, the master data on which their enterprises depend most — their customers, suppliers, the products they sell, the locations where they do business, the assets they manage, the employees who make the business perform. Leaders see the value of having a clear picture, or “best version of the truth,” describing these “master data” entities. But, this is hard to come by with competing priorities, mergers and acquisitions and siloed systems.
As companies grow, business leaders start realizing there is a huge gap between what they do know and what they should know about their customers, suppliers, products, assets and employees. Even worse, most businesses have lost their ability to understand the relationships between business-critical data so they can improve business outcomes. Line of business leaders have been asking questions such as:
- How can we optimize sales across channels when we don’t know which customers bought which products from which stores, sites or suppliers?
- How can we quickly execute a recall when we don’t know which supplier delivered a defective part to which factory and where those products are now?
- How can we accelerate time-to-market for a new drug, when we don’t know which researcher at which site used which combination of compounds on which patients?
- How can we meet regulatory reporting deadlines, when we don’t know which model of a product we manufactured in which lot on which date?
Q. What is the crux of the problem?
A. The crux of the problem is that as businesses grow, their business-critical data becomes fragmented. There is no big picture because it’s scattered across applications, including on premise applications (such as SAP, Oracle and PeopleSoft) and cloud applications (such as Salesforce, Marketo, and Workday). But it gets worse. Business-critical data changes all the time. For example,
- a customer moves, changes jobs, gets married, or changes their purchasing habits;
- a suppliers moves, goes bankrupt or acquires a competitor;
- you discontinue a product or launch a new one; or
- you onboard a new asset or retire an old one.
As all this change occurs, business-critical data becomes inconsistent, and no one knows which application has the most up-to-date information. This costs companies money. It saps productivity and forces people to do a lot of manual work outside their best-in-class processes and world-class applications. One question I always ask business leaders is, “Do you know how much bad data is costing your business?”
Q. What can business leaders do to deal with this issue?
A. First, find out where bad data is having the most significant impact on the business. It’s not hard – just about any employee can share stories of how bad data led to a lost sale, an extra “truck roll,” lost leverage with suppliers, or a customer service problem. From the call center to the annual board planning meeting, bad data results in sub-optimal decisions and lost opportunities. Work with your line of business partners to reach a common understanding of where an improvement can really make a difference. Bad master data is everywhere, but bad master data that has material costs to the business is a much more pressing and constrained problem. Don’t try to boil the ocean or bring a full-blown data governance maturity level 5 approach to your organization if it’s not already seeing success from better data!
Second, focus on the applications and processes used to create, share, and use master data. Many times, some training, a tweak to a process, or a new interface can be created between systems, resulting in very significant improvements for the users without major IT work or process changes.
Lastly, look for a technology that is purpose-built to deal with this problem. Master data management (MDM) helps companies better manage business-critical data in a central location on an ongoing basis and then share that “best version of the truth” with all on premise and cloud applications that need it.
Let’s use customer data as an example. If valuable customer data is located in applications such as Salesforce, Marketo, Seibel CRM, and SAP, MDM brings together all the business-critical data, the core that’s the same across all those applications, and creates the “best version of the truth.” It also creates the total customer relationship view across functions, product lines and regions, which CRM promised but never delivered.
MDM then shares that “mastered” customer data and the total customer relationship view with the applications that want it. MDM can be used to master the relationships between customers, such as legal entity hierarchies. This helps sales and customer service staff be more productive, while also improving legal compliance and management decision making. Advanced MDM products can also manage relationships across different types of master data. For example, advanced MDM enables you to relate an employee to a project to a contract to an asset to a commission plan. This ensures accurate and timely billing, effective expense management, managed supplier spend, and even improved workforce deployment.
When your sales team has the best possible customer information in Salesforce and the finance team has the best possible customer information in SAP, no one wastes time pulling together spreadsheets of information outside of their world-class applications. Your global workforce doesn’t waste time trying to investigate whether Jacqueline Geiger in one system and Jakki Geiger in another system is one or two customers, sending multiple bills and marketing offers at high cost in postage and customer satisfaction. All employees who have access to mastered customer information can be confident they have the best possible customer information available across the organization to do their jobs. And with the most advanced and intelligent data platform, all this information can be secured so only the authorized employees, partners, and systems have access.
Q. Which industries stand to gain the most from mastering their data?
A. In every industry there is some transformation going on that’s driving the need to know people, places and things better. Take insurance for example. Similar to the transformation in the travel industry that reduced the need for travel agents, the insurance industry is experiencing a shift from the agent/broker model to a more direct model. Traditional insurance companies now have an urgent need to know their customers so they can better serve them across all channels and across multiple lines of business.
In other industries, there is an urgent need to get a lot better at supply-chain management or to accelerate new product introductions to compete better with an emerging rival. Business leaders are starting to make the connection between transformation failures and a more critical need for the best possible data, particularly in industries undergoing rapid transformation, or with rapidly changing regulatory requirements.
Q. Which business functions seem most interested in mastering their business-critical data?
A. It varies by industry, but there are three common threads that seem to span most industries:
- MDM can help the marketing team optimize the cross-sell and up-sell process with high quality data about customers, their households or company hierarchies, the products and services they have purchased through various channels, and the interactions their organizations have had with these customers.
- MDM can help the procurement team optimize strategic sourcing including supplier spend management and supplier risk management with high quality data about suppliers, company hierarchies, contracts and the products they supply.
- MDM can help the compliance teams manage all the business-critical data they need to create regulatory reports on time without burning the midnight oil.
Q. How is the use of MDM evolving?
A. When MDM technology was first introduced a decade ago, it was used as a filter. It cleaned up business-critical data on its way to the data warehouse so you’d have clean, consistent, and connected information (“conformed dimensions”) for reporting. Now business leaders are investing in MDM technology to ensure that all of their global employees have access to high quality business-critical data across all applications. They believe high quality data is mission-critical to their operations. High quality data is viewed as the the lifeblood of the company and will enable the next frontier of innovation.
Second, many companies mastered data in only one or two domains (customer and product), and used separate MDM systems for each. One system was dedicated to mastering customer data. You may recall the term Customer Data Integration (CDI). Another system was dedicated to mastering product data. Because the two systems were in silos and business-critical data about customers and products wasn’t connected, they delivered limited business value. Since that time, business leaders have questioned this approach because business problems don’t contain themselves to one type of data, such as customer or product, and many of the benefits of mastering data come from mastering other domains including supplier, chart of accounts, employee and other master or reference data shared across systems.
The relationships between data matter to the business. Knowing what customer bought from which store or site is more valuable than just knowing your customer. The business insights you can gain from these relationships is limitless. Over 90% of our customers last year bought MDM because they wanted to master multiple types of data. Our customers value having all types of business-critical data in one system to deliver clean, consistent and connected data to their applications to fuel business success.
One last evolution we’re seeing a lot involves the types and numbers of systems connecting to the master data management system. In the past, there were a small number of operational systems pushing data through the MDM system into a data warehouse used for analytical purposes. Today, we have customers with hundreds of operational systems communicating with each other via an MDM system that has just a few milliseconds to respond, and which must maintain the highest levels of availability and reliability of any system in the enterprise. For example, one major retailer manages all customer information in the MDM system, using the master data to drive real-time recommendations as well as a level of customer service in every interaction that remains the envy of their industry.
Q. Dennis, why should business leaders consider attending MDM Day?
A. Business leaders should consider attending MDM Day at InformaticaWorld 2014 on Monday, May 12, 2014. You can hear first-hand the business value companies are gaining by using clean, consistent and connected information in their operations. We’re excited to have fantastic customers who are willing to share their stories and lessons learned. We have presenters from St. Jude Medical, Citrix, Quintiles and Crestline Geiger and panelists from Thomson Reuters, Accenture, EMC, Jones Lang Lasalle, Wipro, Deloitte, AutoTrader Group, McAfee-Intel, Abbvie, Infoverity, Capgemini, and Informatica among others.
Last year’s Las Vegas event, and the events we held in London, New York and Sao Paolo were extremely well received. This year’s event is packed with even more customer sessions and opportunities to learn and to influence our product road map. MDM Day is one day before InformaticaWorld and is included in the cost of your InformaticaWorld registration. We’d love to see you there!
See the MDM Day Agenda.
Analyzing current business trends helps illustrate how difficult and complex the Communication Service Provider business environment has become. CSPs face many challenges. Clients expect high quality, affordable content that can move between devices with minimum advertising or privacy concerns. To illustrate this phenomenon, here are a few recent examples:
- Apple is working with Comcast/NBC Universal on a new converged offering
- Vodafone purchased the Spanish cable operator, Ono, having to quickly separate the wireless customers from the cable ones and cross-sell existing products
- Net neutrality has been scuttled in the US and upheld in the EU so now a US CSP can give preferential bandwidth to content providers, generating higher margins
- Microsoft’s Xbox community collects terabytes of data every day making effective use, storage and disposal based on local data retention regulation a challenge
- Expensive 4G LTE infrastructure investment by operators such as Reliance is bringing streaming content to tens of millions of new consumers
To quickly capitalize on “new” (often old, but unknown) data sources, there has to be a common understanding of:
- Where the data is
- What state it is in
- What it means
- What volume and attributes are required to accommodate a one-off project vs. a recurring one
When a multitude of departments request data for analytical projects with their one-off, IT-unsanctioned on-premise or cloud applications, how will you go about it? The average European operator has between 400 and 1,500 (known) applications. Imagine what the unknown count is.
A European operator with 20-30 million subscribers incurs an average of $3 million per month due to unpaid invoices. This often results from incorrect or incomplete contact information. Imagine how much you would have to add for lost productivity efforts, including gathering, re-formatting, enriching, checking and sending invoices. And this does not even account for late invoice payments or extended incorrect credit terms.
Think about all the wrong long-term conclusions that are being drawn from this wrong data. This single data problem creates indirect cost in excess of three times the initial, direct impact of unpaid invoices.
Want to fix your data and overcome the accelerating cost of change? Involve your marketing, CEM, strategy, finance and sales leaders to help them understand data’s impact on the bottom line.
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.
Over the past few years, we have assisted an increasing shift in customer behavior. Pervasive internet connectivity – along with the exponential adoption of mobile devices – has enabled shoppers to research and purchase products of all kinds, anytime and anywhere, using a combination of touch points they find most convenient. This is not a passing fad.
Consumers expect rich data and images to make purchase choices; business users require access to analytical data in order to make mission-critical decisions. These demands for information are driving a need for improved product data availability and accuracy. And this is changing the way businesses go to market.
A staggering number of stores and manufacturers are reforming their models to response to this challenge. The direct-to-consumer (DTC) model, while not new, is rapidly becoming the center stage to address these challenges. The optimal DTC model will vary depending on specific and contextual business objectives. However, there are many strategic benefits to going direct, but the main objectives include growing sales, gaining control over pricing, strengthening the brand, getting closer to consumers, and testing out new products and markets.
It is my contention that while the DTC model is gaining the deserved attention, much remains to be done. In fact, among many challenges that DTC poses, the processes and activities associated with sourcing product information, enriching product data to drive sales and lower returns, and managing product assortments across all channels loom large. More precisely, the challenges that need to be overcome are better exemplified by these points:
- Products have several variations to support different segments, markets, and campaigns.
- Product components, ingredients, care information, environmental impact data and other facets of importance to the customer.
- People are visual. As a result, easy website navigation is essential. Eye-catching images that highlight your products or services (perhaps as they’re being performed or displayed as intended) is an effective way to visually communicate information to your customers and make it easier for them to evaluate options. If information and pictures are readily accessible, customers are more likely to engage.
- Ratings, reviews and social data, stored within the product’s record rather than in separate systems.
- Purchasing and sales measurements, for example, sales in-store, return rates, sales velocity, product views online, as well as viewing and purchasing correlations are often held across several systems. However, this information is increasingly needed for search and recommendation.
The importance of product data and its use, combined with the increased demands on business as a result of inefficient, non-scaling approaches to data management, provide an imperative to considering a PIM to ‘power’ cross-channel retail. Once established, PIM users repeatedly report higher ROI. It is likely that we’ll see PIM systems rank alongside CRM, ERP, CMS, order management and merchandising systems as the pillars of cross-channel retailing at scale.
For all these reasons, choosing the right PIM strategy (and partner) is now a key decision. Get this decision wrong and it could become an expensive mistake.
Research firm Gartner, Inc., sent shockwaves across the technology landscape when it forecast CMOs will spend more on IT than CIOs by 2017[i]. The rationale? “We frequently hear our technology and service provider clients tell us they are dealing with business buyers more, and need to “speak the language.” Gartner itself has fueled this inferno with assertions such as, “By 2017 the CMO will spend more on IT than the CIO” (see “Webinar: By 2017 the CMO Will Spend More on IT Than the CIO”).”[ii] In the two years since Gartner first made that prediction, analysts and pundits have talked about a CIO/CMO battle for data supremacy — describing the two roles as “foes” inhabiting “separate worlds[iii]” that don’t even speak the same language.
But when CIOs are from Mars and CMOs are from Venus, their companies can end up with disjointed technologies that don’t play well together. The result? Security flaws, no single version of “truth,” and regulatory violations that can damage the business. The trick, then, is aligning the CIO and CMO planets.
Informatica’s CMO Marge Breya and CIO Eric Johnson show how they do it.
Q: There’s been a lot of talk lately about how CMOs are now the biggest users of data. That represents a shift in how CMOs and CIOs traditionally have worked together. How do you think the roles of the CMO and CIO need to mesh?
Eric: As I look across the lines of business, and evaluate the level of complexity, the volume of data and the systems we’re supporting, marketing is now by far the most complex part of the business we support. The systems that they have, the data that they have, has grown exponentially over the last four or five years. Now more than ever, [CMOs and CIOs are] very much attached at the hip. We have to be working in conjunction with one another.
Marge: Just to add to that I’d say over the last five years, we’ve been attached to things like CRM systems, or partner relationship systems. From a marketing standpoint, it has really been about management: How do you have visibility into what’s happening with the business. But over the last couple of years it’s become increasingly more important to focus on the “R” word — the relationship: How do you look at a customer name and understand how it relates to their past buying behavior. As a result, you need to understand how information lives from system to system, all across a time series, in order to make really great decisions. The “relate’ word is probably most important, at least in my team right now, and it’s not possible for me to relate data across the organization without having a great relationship with IT.
Q: So how often do you find yourselves talking together?
Eric: We talk to each other probably weekly, and I think our teams work together daily. There’s a constant collaboration and making sure that we’re in sync. You hear about the CIO/CMO relationship. I think it should be an easy relationship because there’s so much going on technology-wise and data-wise that the CMOs are becoming much more technically knowledgeable, and CIOs are starting to understand more and more what’s going on in their business that the line between them should be all about how you work together.
Marge: Of all the business partners in the company, Eric … helps us in marketing reimagine how marketing can be done. If the two of us can go back and forth, understand what’s working and what’s not working, and reimagine how we can be far more effective, or productive or know new things — to me that’s the judge of a healthy relationship between a CIO and a CMO. And luckily, we have that.
Q: It seems as if 2013 was the year of “big data.” But a Gartner survey[iv] said “The adoption is still at the early stages with fewer than 8% of all respondents indicating their organization has deployed big data solutions.” What do you think are the issues that are making it so difficult for companies?
Eric: The concept of big data is something companies want to get involved in. They want to understand how they can leverage this fast-growing volume of data from various sources. But the challenge is being able to understand what you’re looking for, and to know what kind of questions you have.
Marge: There’s a big focus on big data, almost for the sake of it in some cases. People get confused about whether it’s about the haystack, or the needle. Having a haystack for the heck of it isn’t usually what’s done. It’s for a purpose. It’s important to understand what part of that haystack is important for what part of your business. How up-to-date is it? How much can you trust the data. How much can you make real decisions from it. And frankly, who should have access to it. So much of the data we have today is sensitive, affected by privacy laws and other kinds of regulations. I think big data is appropriately a great term right now, but more importantly, it’s not just about big data, it’s about great data. How are you going to use it? And how it’s going to affect your business process.
Eric: You could go down into a rat hole if you’re chasing something and you’re not really sure what you’re going to do with it.
Marge: On the other hand, you can explore years of behavior and maybe come up with a great predictive model for what a new buying signal scoring engine could look like.
Q: One promise of big data is the ability to pull in data from so many sources. That would suggest a real need for you two to work together to ensure the quality and the integrity of the data. How do you collaborate on those issues?
Eric: There’s definitely a lot of work that has to be done working with the CMO and the marketing organization: To sit down and understand where’s this data coming from, what’s it going to be used for, and making sure you have the people and processing components. Especially with the level of complexity we have, with all the data coming in from so many sources, making sure that we really map that out, understand the data and what it looks like and what some of the challenges could be. So it’s partnering very closely with marketing to understand those processes, understand what they want to do with the data, and then putting the people, the processes and the technology in place so you can trust the data and have a single source of truth.
Marge: You hit the nail on the head with “people, process and technology.” Often, folks think of database quality or accuracy as being an IT problem. It’s a business problem. Most people know their business, they know what their data should look like. They know what revenue shapes should look like. What’s norm for the business. If the business people aren’t there from a governance standpoint, from a stewardship standpoint — literally saying “does this data make sense?” — without that partnership, forget it.
Gartner does a nice job of describing the digital landscape that marketers are facing today in its infographic below. In order to use technology as a differentiator, organizations need to get the most value from their data. The relationships between these technology is going to make the difference between organizations that gain a competitive advantage from their operations and the laggards.
[i] Gartner Research, December 20, 2013, “Market Trends: The Rising Importance of the Business Buyer – Fact of Fiction?” Derry N. Finkeldey
[ii] Gartner Research, December 20, 2013, “Market Trends: The Rising Importance of the Business Buyer – Fact of Fiction?” Derry N. Finkeldey
[iii] Gartner blog, January 25, 2013, “CMOs: Are You Cheating on Your CIO?”, Jennifer Beck, Vice President & Gartner Fellow
[iv] Gartner Research, September 12, 2013, “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype,” Lisa Kart, Nick Heudecker, Frank Buytendijk
Which comes first: innovation or analytics?
Bain & Company released some survey findings a few months back that actually put a value on big data. Companies with advanced analytic capabilities, the consultancy finds, are twice as likely to be in the top quartile of financial performance within their industries; five times as likely to make decisions much faster than market peers; three times as likely to execute decisions as intended; and twice as likely to use data very frequently when making decisions.
This is all good stuff, and the survey, which covered the input of 400 executives, makes a direct correlation between big data analytics efforts and the business’s bottom line. However, it begs a question: How does an organization become one of these analytic leaders? And there’s a more brain-twisting question to this as well: would the type of organization supporting an advanced analytics culture be more likely to be ahead of its competitors because its management tends to be more forward-thinking on a lot of fronts, and not just big data?
You just can’t throw a big data or analytics program or solution set on top of the organization (or drop in a data scientist) and expect to be dazzled with sudden clarity and insight. If an organization is dysfunctional, with a lot of silos, fiefdoms, or calcified and uninspired management, all the big data in the world isn’t going to lift its intelligence quota.
The author of the Bain and Company study, Travis Pearson and Rasmus Wegener, point out that “big data isn’t just one more technology initiative” – “in fact, it isn’t a technology initiative at all; it’s a business program that requires technical savvy.”
Succeeding with big data analytics requires a change in the organization’s culture, and the way it approaches problems and opportunities. The enterprise needs to be open to innovation and change. And, as Pearson and Wegener point out, “you need to embed big data deeply into your organization. It’s the only way to ensure that information and insights are shared across business units and functions. This also guarantees the entire company recognizes the synergies and scale benefits that a well-conceived analytics capability can provide.”
Pearson and Wegener also point to the following common characteristics of big data leaders they have studied:
Pick the “right angle of entry”: There are many areas of the business that can benefit from big data analytics, but just a few key areas that will really impact the business. It’s important to focus big data efforts on the right things. Pearson and Wegener say there are four areas where analytics can be relevant: “improving existing products and services, improving internal processes, building new product or service offerings, and transforming business models.”
Communicate big data ambition: Make it clear that big data analytics is a strategy that has the full commitment of management, and it’s a key part of the organization’s strategy. Messages that need to be communicated: “We will embrace big data as a new way of doing business. We will incorporate advanced analytics and insights as key elements of all critical decisions.” And, the co-authors add, “the senior team must also answer the question: To what end? How is big data going to improve our performance as a business? What will the company focus on?”
Sell and evangelize: Selling big data is a long-term process, not just one or two announcements at staff meetings. “Organizations don’t change easily and the value of analytics may not be apparent to everyone, so senior leaders may have to make the case for big data in one venue after another,” the authors caution. Big data leaders, they observe, have learned to take advantage of the tools at their disposal: they “define clear owners and sponsors for analytics initiatives. They provide incentives for analytics-driven behavior, thereby ensuring that data is incorporated into processes for making key decisions. They create targets for operational or financial improvements. They work hard to trace the causal impact of big data on the achievement of these targets.”
Find an organizational “home” for big data analysis: A common trend seen among big data leaders is that they have created an organizational home for their advanced analytics capability, “often a Center of Excellence overseen by a chief analytics officer,” according to Pearson and Wegener. This is where matters such as strategy, collection and ownership of data across business functions come into play. Organizations also need to plan how to generate insights, and prioritize opportunities and allocation of data analysts’ scientists’ time.
There is a hope and perception that adopting data analytics will open up new paths to innovation. But it often takes a innovative spirit to open up analytics.
Financial services is one of the most data-centric industries in the world. Clean, connected, and secure data is critical to satisfy regulatory requirements, improve customer experience, grow revenue, avoid fines, and ultimately change the world of banking and insurance. Data management improvements have been made and several of the leading companies are empowered by Informatica.
Who are these companies and what are they doing with Informatica?
Fifteen of the top financial services companies will share their stories and success leveraging Informatica for their most critical business needs. These include:
- Capital One
- Bank of New Zealand
- Fannie Mae
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- Morgan Stanley
- Thomson Reuters
- YAPI KREDI BANKASI A.S.
- Navy Federal Credit Union
- Wells Fargo Bank
- Westpac Banking Corporation
- Great American Insurance Group, Property & Casualty Group
- Liberty Mutual
Informatica World 2014 will have over 100 breakout sessions covering a wide range of topics for Line of Business Executives, IT decision makers, Architects, Developers, and Data Administrators. Our great keynote line up includes Informatica executives Sohaib Abbasi (Chief Executive Officer), Ivan Chong (Chief Strategy Officer), Marge Breya (Chief Marketing Officer) and Anil Chakravarthy (Chief Product Officer). Our series of speakers will share Informatica’s vision for this new data-centric world and explain innovations that will propel the concept of a data platform to an entirely new level.
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“If I had my way, I’d fire the statisticians – all of them – they don’t add value”.
Surely not? Why would you fire the very people who were employed to make sense of the vast volumes of manufacturing data and guide future production? But he was right. The problem was at that time data management was so poor that data was simply not available for the statisticians to analyze.
So, perhaps this title should be re-written to be:
Fire your Data Scientists – They Aren’t Able to Add Value.
Although this statement is a bit extreme, the same situation may still exist. Data scientists frequently share frustrations such as:
- “I’m told our data is 60% accurate, which means I can’t trust any of it.”
- “We achieved our goal of an answer within a week by working 24 hours a day.”
- “Each quarter we manually prepare 300 slides to anticipate all questions the CFO may ask.”
- “Fred manually audits 10% of the invoices. When he is on holiday, we just don’t do the audit.”
This is why I think the original quote is so insightful. Value from data is not automatically delivered by hiring a statistician, analyst or data scientist. Even with the latest data mining technology, one person cannot positively influence a business without the proper data to support them.
Most organizations are unfamiliar with the structure required to deliver value from their data. New storage technologies will be introduced and a variety of analytics tools will be tried and tested. This change is crucial for to success. In order for statisticians to add value to a company, they must have access to high quality data that is easily sourced and integrated. That data must be available through the latest analytics technology. This new ecosystem should provide insights that can play a role in future production. Staff will need to be trained, as this new data will be incorporated into daily decision making.
With a rich 20-year history, Informatica understands data ecosystems. Employees become wasted investments when they do not have access to the trusted data they need in order to deliver their true value.
Who wants to spend their time recreating data sets to find a nugget of value only to discover it can’t be implemented?
Build a analytical ecosystem with a balanced focus on all aspects of data management. This will mean that value delivery is limited only by the imagination of your employees. Rather than questioning the value of an analytics team, you will attract some of the best and the brightest. Then, you will finally be able to deliver on the promised value of your data.