Information workers in India are increasingly using their personal devices, applications, and web services to accomplish both personal and work-related activities. Results from Forrester’s Forrsights Workforce Employee Survey, Q4 2012 indicate that at least 85% of employees use phone/tablet applications and web-based services for both purposes which is putting corporate information security under serious threat.
My interactions with numerous infrastructure and operations (I&O) professionals from large enterprises in India over the past six months have revealed that there is a high degree of awareness of the need to develop a bring-your-own-technology (BYOT) policy. However, actual implementations aren’t yet common, as I&O professionals are unable to address management’s three key concerns. These are, in order of priority:
How can we ensure that information on employee-owned hardware and software is secure?
Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.
So, do big data gurus have it wrong? Sort of . . .
I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:
Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.
It is easy to get caught up in the source and target paradigm when implementing master data management. The logical model looms large to identify where master data resides for linkage and makes the project -- well -- logical.
If this is the first step in your customer MDM endeavor and creating a master data definition based on identifying relevant data elements, STOP!
The first step is to articulate the story that customer MDM will support. This is the customer MDM blueprint.
For example, if the driving business strategy is to create a winning customer experience, customer MDM puts the customer definition at the center of what the customer experience looks like. The customer experience is the story. You need to understand and have data points for elements such as preferences, sentiment, lifestyle, and friends/relationships. These elements may be available within your CRM system, in social networks, with partners, and third-party data providers. The elements may be discrete or derived from analytics. If you only look for name, address, phone, and email, there is nothing about this definition that helps determine how you place that contact into context of engagement.
Ultimately, isn’t that what the business is asking for when they want the promised 360-degree view of the customer? Demands for complete, relevant, and timely are not grounded in the databases, data dictionaries, and integration/transformation processes of your warehouses and applications; they are grounded in the story.
So, don’t start with the data. Start with the story you want to tell.
Information workers in organizations across Asia Pacific (AP) are increasingly using personal mobile devices, applications, and public cloud services for work. Forrester defines this as the bring-your-own-technology (BYOT) trend. This behavior is more prevalent among employees above the director-level (C-level executives, presidents, and vice presidents) than those below that level (individual worker, contractor or consultant and manager/supervisor). Data from Forrester’s Forrsight Workforce survey, Q4 2012 corroborates this trend in AP.
We believe that the BYOT trend will strengthen over the next two years in AP, primarily fueled by employees below the director level. Increasing options, quality and affordability of devices, apps, and wireless connectivity, coverage, and capacity will contribute to this expansion. In order to secure corporate data, organizations will need to:
Develop Corporate Mobile Policies: Organizations must build cross-functional teams to plan their mobile strategies. This should include representatives from different LOBs like finance, HR, legal and sourcing. Moreover, the policy must clearly define guardrails to provide flexibility to employees but within boundaries and in compliance with local regulations.
Identify Technologies To Secure Corporate Data: 29% of business-decision makers in AP report that the rising expectations of younger workers require businesses to push enterprise IT to keep technology current. This is why it is critical to identify both back-end and front-end technologies and suppliers that can optimize mobile device and application management in a secure manner. Focus should be on networking layer security and mobile device management solutions.
There is a shift underway with master data management (MDM) that can't be ignored. It is no longer good enough to master domains in a silo and think of MDM as an integration tool. First-generation implementations have provided success to companies seeking to manage duplication, establishing a master definition, and consolidating data into a data warehouse. All good things. However, as organizations embrace federated environments and put big data architectures into wider use, these built-for-purpose MDM implementations are too narrowly focused and at times as rigid as the traditional data management platforms they support.
Yet, it doesn't have to be that way. By nature, MDM is meant to provide flexibility and elasticity to managing both single and multiple master domains. First, MDM has to be redefined from a data integration tool to a data modeling tool. Then, MDM is better aligned to business patterns and information needs, as it is designed by business context.
Enter The Golden Profile
When the business wants to put master data to use it is about how to have a view of a domain. The business doesn't think in terms of records, it thinks about using the data to improve customer relationships, grow the business, improve processes, or any host of other business tasks and objectives. A golden profile fits this need by providing the definition and framework that flexes to deliver master data based on context. It can do so because it is driven by data relationships.
Across Asia Pacific (AP), expanding mobility support for employees, customers, and/or business partners will be the top strategic telecom priority for enterprises in 2013, surpassing other telecom priorities like performing network management and consolidating operations equipment, rationalizing/consolidating telecom/communications service providers, and moving communications applications to the cloud.
While enterprises will invest in a range of mobility products and services, there are five key areas in particular which will attract the most investment in 2013. Vendors need to focus on the solutions and engagement models that meet customers’ needs in these five areas and target the industries and countries where the demand will be greatest:
Business consulting services. Specifically for defining a formal enterprise mobility and/or BYOD program strategy, including devices, applications, data access, and provisioning. Moreover, AP organizations will likely need help in drafting compliance and legal policies related to enterprise mobility.
Telecom expense management solutions. This is one of the most critical telecom requirements for AP CIOs in 2013. Across the region, 50% to 60% of organizations pay the entire cost of voice and data services for company-supported Android and iOS phones and tablets. For BlackBerry phones, this proportion is nearly 70%.
I recently had a client ask about MDM measurement for their customer master. In many cases, the discussions I have about measurement is how to show that MDM has "solved world hunger" for the organization. In fact, a lot of the research and content out there focused on just that. Great to create a business case for investment. Not so good in helping with the daily management of master data and data governance. This client question is more practical, touching upon:
what about the data do you measure?
how do you calculate?
how frequently do you report and show trends?
how do you link the calculation to something the business understands?
I just came back from a Product Information Management (PIM) event this week had had a lot of discussions about how to evaluate vendors and their solutions. I also get a lot of inquiries on vendor selection and while a lot of the questions center around the functionality itself, how to evaluate is also a key point of discussion. What peaked my interest on this subject is that IT and the Business have very different objectives in selecting a solution for MDM, PIM, and data quality. In fact, it can often get contentious when IT and the Business don't agree on the best solution.
General steps to purchase a solution seem pretty consistent: create a short list based on the Forrester Wave and research, conduct an RFI, narrow down to 2-3 vendors for an RFP, make a decision. But, the devil seems to be in the details.
Is a proof of concept required?
How do you make a decision when vendors solutions appear the same? Are they really the same?
How do you put pricing into context? Is lowest really better?
What is required to know before engaging with vendors to identify fit and differentiation?
When does meeting business objectives win out over fit in IT skills and platform consistency?
Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.
I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.
If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:
The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
The number one reason I hear from IT organizations for why they want to embark on MDM is for consolidation or integration of systems. Then, the first question I get, how do they get buy-in from the business to pay for it?
My first reaction is to cringe because the implication is that MDM is a data integration tool and the value is the matching capabilities. While matching is a significant capability, MDM is not about creating a golden record or a single source of truth.
My next reaction is that IT missed the point that the business wants data to support a system of engagement. The value of MDM is to be able to model and render a domain to fit a system of engagement. Until you understand and align to that, your MDM effort will not support the business and you won’t get the funding. If you somehow do get the funding, you won’t be able to appropriately select the MDM tool that is right for the business need, thus wasting time, money, and resources.
Here is why I am not a fan of the “single source of truth” mantra. A person is not one-dimensional; they can be a parent, a friend, or a colleague, and each has different motivations and requirements depending on the environment. A product is as much about the physical aspect as it is about the pricing, message, and sales channel it is sold through. Or, it is also faceted by the fact that it is put together from various products and parts from partners. In no way is a master entity unique or has a consistency depending on what is important about the entity in a given situation. What MDM provides are definitions and instructions on the right data to use in the right engagement. Context is a key value of MDM.