Tag Archives: Gartner
Several months ago, I was talking to some CIOs about their business problems. During these conversations, I asked them about their interest in Big Data. One sophisticated CIO recoiled almost immediately saying that he believes most vendors are really having a problem discussing “Big Data” with customers like him. It would just be so much easier if you guys would talk to me about helping my company with our structured data and unstructured data. At the same time, Gartner has found that 64% of enterprises surveyed say they’re deploying or planning to deploy a Big Data project. The problem is that 56% of those surveyed by Gartner are still struggling to determine how to get value out of big data projects and 23% are struggling with the definition of what is Big Data and what is not Big Data.
Clearly, this says the term does not work with market and industry participants. To me this raises a question about the continued efficacy of the term. And now, Thomas Davenport, the author of “Competing on Analytics”, has suggested that we retire the term all together. Tom says that in his research “nobody likes the term”. He claims in particular that executives yearn for a better way to communicate what they are doing with data and analytics.
Tom suggests in particular that “Big Data” has five significant flaws:
1) Big is relative. What is big today will not be so large tomorrow. Will we have to tall call the future version Big Big Data?
2) Big is only one aspect of what is distinctive about the data in big data. Like my CIO friend said it is not as much about the size of data as it is about the nature of the data. Tom says bigness demands more powerful services, but a lack of structure demands different approaches to process the data.
3) Big data is defined as having volume, variety, and velocity. But what do you call data that has variety and velocity but the data set is not “big”.
4) What do you call the opposite of big data? Is it small data? Nobody likes this term either.
5) Too many people are using “big data” incorrectly to mean any use of analytics, reporting, or conventional business intelligence.
Tom goes onto say, “I saw recently, over 80 percent of the executives surveyed thought the term was overstated, confusing, or misleading”. So Tom asks why don’t we just stop using it. In the end, Tom struggles with ceasing his use of the term because the world noticed the name Big Data unlike other technological terms. Tom has even written a book on the subject—“Big Data at Work”. The question I have is do we in the IT industry want to really lose all the attention. It feels great to be in the cool crowd. However, CIOs that I have talked to say they are really worried about what will happen if their teams oversell Big Data and do not deliver tangible business outcomes. The reality Tom says it would be more helpful than saying, we are cool and we are working on big data to instead say instead we’re extracting customer transaction data from our log files in order to help marketing understand the factors leading to customer attrition”. I tend to agree with this thought but I would like to hear what you think? Should we as an industry retire the term Big Data?
Author Twitter: @MylesSuer
Business leaders share with Fortune Magazine their view of Big Data
Fortune Magazine recently asked a number of business leaders about what Big Data means to them. These leaders provide three great stories for the meaning of Big Data. Phil McAveety at Starwood Hotels talked about their oldest hotel having a tunnel between the general manager’s office and the front desk. This way the general manager could see and hear new arrivals and greet each like an old friend. Phil sees Big Data as a 21st century version of this tunnel. It enables us to know our guests and send them offers that matter to them. Jamie Miller at GE says Big Data is being about transforming how they service their customers while simplifying the way they run their company. Finally, Ellen Richey at VISA says that big data holds the promise of making new connections between disperse bits of information creating value.
Everyone is doing it but nobody really knows why?
I find all of these definitions interesting, but they are all very different and application specific. This isn’t encouraging. The message from Gartner is even less so. They find that “everyone is doing it but nobody really knows why”. According to Matt Asay, “the gravitational pull of Big Data is now so strong that even people who haven’t a clue as to what it’s all about report that they are running Big Data projects”. Gartner found in their research that 64% of enterprises surveyed say they’re deploying or planning to deploy Big Data projects. The problem is that 56% of those surveyed are struggling trying to determine how to get value out of big data, and 23% of those surveyed are struggling at how to define Big Data. Hopefully, none of the latter are being counted in the 64%. . Regardless, Gartner believes that the number of companies with Big Data projects is only going to increase. The question is how many of projects are just a recast of an existing BI project in order to secure funding or approval. No one will ever know.
Managing the hype phase of Big Data
One CIO that we talked to worries about this hype phase of Big Data. He says the opportunity is to inform analytics and guiding and finding business value. However, worries whether past IT mistakes will repeat themselves. This CIO believes that IT has gone through three waves. IT has grown from homegrown systems to ERP to Business Intelligence/Big Data. ERP was supposed to solve all the problems of the homegrown solutions but it did not provide anything more than information on transactions. You could not understand what is going on out there with ERP. BI and Big Data is trying to go after this. However, this CIO worries that CEOs/CFOs will soon start complaining that the information garnered does not make the business more money. He worries that CEOs and CFOs will start effectively singing the Who song, “We won’t get fooled again.”
This CIO believes that to make more money, Big Data needs to connect the dots between transactional systems, BI, and planning systems. It needs to convert data into business value. This means Big Data is not just another silo of data, but needs to be connected and correlated to the rest of your data landscape to make it actionable. To do this, he says it needs to be proactive and cut the time to execution. It needs to enable the enterprise to generate value different than competitors. This, he believes mean that it needs to orchestrate activities so they maximize profit or increase customer satisfaction. You need to get to the point where it is sense and response. Transactional systems, BI, and planning systems need to provide intelligence to allow managers to optimize business processes execution. According to Judith Hurwitz, optimization is about establishing the correlation between streams of information and matching the resulting pattern with defined behaviors such as mitigating a threat or seizing an opportunity.”
Don’t leave your CEO and CFO with a sense of deja vu
In sum, Big Data needs to go further in generating enough value to not leave your CEO and CFO with a sense of deja vu. The question is do you agree? Do you personally have a good handle on what Big Data is? And lastly, do you fear a day when the value generated needs to be attested to?
Last week I had the opportunity to attend the Gartner Security and Risk Management Summit. At this event, Gartner analysts and security industry experts meet to discuss the latest trends, advances, best practices and research in the space. At the event, I had the privilege of connecting with customers, peers and partners. I was also excited to learn about changes that are shaping the data security landscape.
Here are some of the things I learned at the event:
- Security continues to be a top CIO priority in 2014. Security is well-aligned with other trends such as big data, IoT, mobile, cloud, and collaboration. According to Gartner, the top CIO priority area is BI/analytics. Given our growing appetite for all things data and our increasing ability to mine data to increase top-line growth, this top billing makes perfect sense. The challenge is to protect the data assets that drive value for the company and ensure appropriate privacy controls.
- Mobile and data security are the top focus for 2014 spending in North America according to Gartner’s pre-conference survey. Cloud rounds out the list when considering worldwide spending results.
- Rise of the DRO (Digital Risk Officer). Fortunately, those same market trends are leading to an evolution of the CISO role to a Digital Security Officer and, longer term, a Digital Risk Officer. The DRO role will include determination of the risks and security of digital connectivity. Digital/Information Security risk is increasingly being reported as a business impact to the board.
- Information management and information security are blending. Gartner assumes that 40% of global enterprises will have aligned governance of the two programs by 2017. This is not surprising given the overlap of common objectives such as inventories, classification, usage policies, and accountability/protection.
- Security methodology is moving from a reactive approach to compliance-driven and proactive (risk-based) methodologies. There is simply too much data and too many events for analysts to monitor. Organizations need to understand their assets and their criticality. Big data analytics and context-aware security is then needed to reduce the noise and false positive rates to a manageable level. According to Gartner analyst Avivah Litan, ”By 2018, of all breaches that are detected within an enterprise, 70% will be found because they used context-aware security, up from 10% today.”
I want to close by sharing the identified Top Digital Security Trends for 2014
- Software-defined security
- Big data security analytics
- Intelligent/Context-aware security controls
- Application isolation
- Endpoint threat detection and response
- Website protection
- Adaptive access
- Securing the Internet of Things
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.
Gartner Estimates Growth for MDM Market and Positions Informatica as a Leader in The Magic Quadrant for Customer Data Solutions
Over the last few weeks I’ve been saying that the incredible popularity of our MDM-related events is a sign that MDM is a vital and growing market. Now I consider it reassuring that analysts agree. It’s also reassuring to see Informatica positioned as a Leader in the Magic Quadrant for Master Data Management of Customer Data Solutions, a position that Informatica has held for four years in a row.
In Gartner’s October 2013 Magic Quadrant for Master Data Management of Customer Data Solutions, analysts Bill O’Kane and Saul Judah estimates that the total software revenue for packaged MDM solutions was $1.6B in 2012, an increase of 7.8% from 2011, as compared with a 4.7% rise for the overall enterprise software market. Further, O’Kane and Judah estimate that the MDM of customer data solutions market segment was worth $527M in 2012, an increase of 5.4% from 2011. The analysts go on to say that the customer MDM market is far from mature, and that just 40% of the organizations surveyed by Gartner were beginning MDM initiatives.
One of MDM’s most important benefits is a single view of the customer across company departments and siloed systems. In this Magic Quadrant report, the analysts describe some of the business drivers for obtaining this view. For the banking and life sciences sectors, the analysts include “Compliance and risk management drivers, such as ‘know your customer,’ anti-money laundering and counterparty risk management in the banking sector, and Sunshine Act compliance in the life sciences sector.” I believe that many other industries could similarly benefit from trusted customer interactions. Another set of drivers they list are “cost optimization and efficiency drivers,” and finally, “revenue and profitability growth drivers,” explaining that examples of such drivers include “initiatives to improve cross-selling, upselling, and retention.”
Finally, the analysts observed a trend which we believe supports Informatica’s view of the importance of all-encompassing MDM solutions that can manage master data across enterprise. As noted in the report, “Many organizations have now invested in creating a new central system to master their customer data, with the majority (an estimated 80%) of organizations buying packaged MDM of customer data solutions, as opposed to building the capability themselves.”
To learn more about Gartner’s October 2013 Magic Quadrant for Master Data Management of Customer Data Solutions, see our press release or download the full report. After reading the report, please share your thoughts below.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
The general feeling, at the recent Gartner Master Data Management Summit, was one of excitement. It wasn’t just that this was the largest MDM Summit to date with 600+ registrants; it was also the buzz in and around the convention center. The conversation wasn’t about promises and “what ifs,” it was about tactics, and 2nd or even 3rd generation MDM initiatives. This growth is a sign that MDM has matured past the initial phase of high expectations, in Gartner’s hype cycle. To put it in Geoffrey Moore’s terms, I think it’s a sign that MDM has crossed the chasm from early adoption to more widespread, pragmatic adoption.
According to a Forbes article, the average organization will grow their data by 50 percent in the coming year. Overall corporate data is expected to grow by 94 percent. According to Informatica, data is predicted to increase by as much as 75 times the current volume by the year 2020. What is Big Data all about? Big Data is the management and analytics of an immensely growing volume, variety, and velocity of data in a digital world. A precise definition of big data from analysts like Gartner and Forrester is a hot topic right now that is covered in a lot of blogs.
In my point of view, big data is connecting the dots. It is connecting more than ever before. But what is the role of product data in a big data world?
After recently talking to our customer Halfords, the UK retailer for bicycle and auto parts revealed: All challenge Amazon. Halfords is known as the expert and friend for cyclists. Therefore they position their brand as the leading expert with the best information. They use product information as a differentiator in the market to gain customers’ trust.
This article refers to a challenge that a lot of distributors and retailers are facing. In order to better serve their B2B and B2C customers, they grow and position their product range to be the one trusted supplier. The long tail (endless aisle) strategy offers higher margins with niche products as well.
These distributors and retailers are faced with the challenge of handling 100s or 1000s of suppliers providing content for millions of products. What happens when different suppliers provide information for the same product?
A business case of big product data: Innovative distributors attempt to merge different product content to create the best and richest product information. This requires an intelligent analysis of a supplier’s product data, and intelligent automatism in order to merge this data to create superior product content. The role of the data steward in defining these rules and policies becomes more important than ever before.
How can this be solved?
Data doesn’t only come from suppliers but from other data sources as well. Basic product information might come from a data hub like GS1 or could be synchronized from the distributor’s ERP system, which in turn might be leading the creation of new products in the distributor’s master assortment.
This basic data will be enriched by data coming directly from the manufactures or the suppliers of the distributor. These different data sources provide content for the same products in different levels of quality, richness, and completeness.
Which parts of product information are used from which data sources is determined by objective data quality rules combined with a definition of trust specific to each data source. One supplier is known for accurate descriptions in English while another provides the better German information. And yet a third data source usually provides the best images.
Governance of Product Information Creates Competitive Advantages
This is when Product Information Management comes into the field: to control big product data. According to Heiler’s PIM Product Manager, Markus Schuster, these business processes can only be successful when used with intelligent, highly automated data quality proofpoints and workflows that adhere to the data governance policy.
I’m at Barcelona this week for the European Gartner MDM Summit. I had a chance to catch up with one of the Gartner MDM analysts before the event, and we had a discussion about the growth of MDM. He mentioned that MDM will become pervasive within the enterprise as organizations expand its use as a necessary foundation for governing all of their business-critical master data such as customers, products, and so on.
To solve their business problems accurately, companies seek targeted MDM solutions. For e.g., retail, distribution, and manufacturing companies use PIM for merchandising, distributing products, and supplier on-boarding, while financial services, healthcare, and high tech companies use customer MDM with their CRM, such as salesforce.com, for improving customer segmentation, cross-sell , and up-sell. (more…)
Has big data entered the “trough of disillusionment?” That’s what I’ve heard recently. Like many hyped up technology trends the trough can be deep and long as project failures accumulate, or for ‘hot’ trends that evolve and mature quickly the trough can be shallow and short, leading to broader and rapid adoption. Is the big data hype failing to deliver on its promise of increased revenue and competitive advantage for companies that leverage big data to introduce new products and services and improve business operations? Why is it that some big data projects fail to deliver on their promise? Svetlana Sicular, Research Director, Gartner points out in her blog Big Data is Falling into the Trough of Disillusionment that, “These [advanced client] organizations have fascinating ideas, but they are disappointed with a difficulty of figuring out reliable solutions.” There are several reasons why big data projects may fail to deliver on their promise: (more…)