Highly Competitive - software industry insights

Master Data Management Rebooted

 

Part 1 of a multi-part series on the "New Age of Master Data Management"

 

The 'New Age' of Master Data Management

It's about time that master data management (MDM) enjoyed a 'new age'. The creation and use of master data for business purposes has always been a good idea. The goal has been to make sense of all data that touches the organization: clean it up, add context, enrich it, and put it to work strategically as well as operationally.

Unfortunately, MDM has been plagued with certain problems from the very start. This stems mostly from the fact that MDM lands hard on some of the messiest aspects of an organization: data silos, unconnected systems and processes, management turf wars, and organizational roadblocks to disparate people working effectively together for common purpose.

 

Why Does Master Data Matter?

As the importance, value and sheer volume of data assets grow, so does the critical need for trustworthy, up-to-date information that is ready-to-go for any purpose. MDM processes consolidate data from numerous sources in meaningful ways. Master data also provides context and reveals data relationships for sources like big data that often exist as fragments without useful referential connections.

Master data has a large and growing role in areas that matter to organizations: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys.

Analytics, particularly of the real-time variety, produce more realistic and trustworthy results when utilizing master data. Master data is often a key part of predictive analytics in support of: supply chain processes; personalization for improved customer experiences; logistics; operational optimization; and safety and preventive procedures.

A core element of MDM is data quality. Improved data quality impacts endless aspects of the business for reasons that should be obvious. But all too often, business management fails to comprehend how much poor data quality impacts profitability, productivity and even perception of the brand. Real world incidents that have been repeated at far too many organizations include:

• Inconsistent data of poor quality has led to extensive quantities of items in warehouses to "disappear" from systems records, never to be sold or consumed by the business. This can amount to millions of dollars of losses.
• Contradictory and confusing product lists and catalogs that deter purchases by customers and make sales staff look stupid – again with no purchases resulting.
• Costly pricing errors that persist undetected over time can have breath-taking negative impact on the bottom line.

 

Roadblocks, Detours, Dead Ends

Over time traditional approaches for MDM implementations have faltered for several reasons, many of which echo the failures of traditional ERP installations:

• Implementations take too long and cost too much.
• Project scope is often much too large.
• Business and IT teams are poorly coordinated for achieving the desired end state.
• Results fail to meet important business needs and / or show value in an obvious and timely fashion.
• Traditional approaches lag in keeping up with new technologies.
• Solutions have delayed including new data sources like the many varieties of big data.

A continuing obstacle is the lack of involvement of upper management with such important activities. If organizations want to tap into more data that is trustworthy and business-ready, it requires buy-in from upper management on down. Such buy-in is shown through clear commitments, upper management champions, and real data strategies – basically understanding the high value of data and what it takes to achieve it.

 

The Reality of MDM: People and Politics

What many people get wrong about MDM is thinking that it's all about the technology. In reality, it's much more about people, processes, and (unfortunately) politics. The people and politics of the organization can throw up significant obstacles to successful MDM programs. This is more likely to happen in organizations where upper level management has failed to provide its leadership and involvement in MDM implementations.

Silos permeate organizations from top to bottom, and not just in terms of software systems or digital assets. Collaboration between people is often limited. Internal politics can bring on secrecy or "turf wars" when it comes to the ownership of data and business processes. Resistance to change, lack of trust, desires to "hoard" data and information all come into play. Orchestration and collaboration of business and technical teams have always been critical to MDM success, but can be derailed by internal silos and politics.

Of course, technology provides an important means to achieve MDM implementations. But many vital activities aren't technical. For MDM and data governance to be successful, distinct changes must take place in organizations to eliminate the many silos that prevent the generation of reliable and up-to-date master data.

Not surprisingly, when things go wrong, organizations want to blame MDM technologies for the failures, instead of facing up to what really ails the organization. However, traditional MDM solutions have had significant limitations and often don't do much to help with the "people problem".

 

Wanted: Improved Approaches

A classic approach to MDM, the "bottom up" method, has not worked well simply because unsynchronized teams are trudging separate long roads to achieve the "desired end state" and often don't ever get there. It's easy to imagine the many ways that this method can be derailed. Instead, businesses benefit most from MDM approaches that work the way the business and its employees need to function to achieve real goals and objectives.

One of the most important changes to MDM approaches has organically grown out of the fact that much of MDM and data governance revolves around business use cases and business users. So it's gratifying to see more overtly business-defined approaches that not only delineate the approach but are enabling changes to the technologies that support them. These business-defined approaches reflect the complex, sophisticated needs of many organizations:

• Working from the desired end state and / or from the requirements of complex business problems to create master data management processes.
• Choosing contextual and analytic MDM solutions over traditional MDM tools that can handle new approaches for multi-dimensional and complex hierarchical data, including social and commercial graphs that underlie business use cases such as customer-responsive interactions across digital channels.
• Utilizing new MDM platforms that simplify the development of industry-focused business applications that take advantage of master data to solve business problems.
• Transforming model-driven approaches to be more flexible and agile in order to work with existing business processes as the starting point (instead of creating new processes), to deliver business value more quickly. IT and business teams can then transition to optimizing various processes for additional value.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In a real world example, a pharmaceuticals company has utilized a new MDM solution to uncover significant information that improves the success of promoting drugs to individual healthcare organizations. Such information includes:

• Which insurance companies or payers accepted by the healthcare organization actually cover the drugs
• And for those payers, which insurance plans provide coverage
• What contracts for the pharmaceuticals company are already in place
• What other sales teams in the same company are already promoting to the healthcare organization
• Other important influence and decision-making factors that derive from complex hierarchies and networks of relationships

Running through such an analysis based on master data before approaching a healthcare organization helps the sales team determine if any business opportunities exist, and reveals better ways to approach contacts in the organization.

 

Master Data Management Rebooted

In the next five posts, I'll present and analyze what is changing for master data management approaches and solutions, including new technologies, and the evolutionary involvement of business roles in MDM and data governance.

One of the final aspects that I'll explore is this: is it time to quit calling it "master data management" and devise a name that better fits MDM Rebooted?

 

Image Source: hbr.org

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

The Missing Pieces of Master Data Management Services

 

A master data initiative is meant to deliver a unified and well-integrated source of cross-organizational data that is reliable and up-to-date, while eliminating silos and redundancies across the organization. Master data management (MDM) software solutions comprise an essential piece for trustworthy analytics that lead to better decision-making. Master data is also a much-needed accompaniment for big data analytics, providing critical context for big data, and as a means to connect big data analytics to business objectives and value.

We're seeing more offerings for MDM services from IT and solutions consultants, managed services providers, and software vendors – with cloud platforms playing a clear role in these offerings. Third party technology services often result in lower costs and faster time to solution use and to the engendering of business value. Unfortunately providers of MDM services frequently only target IT groups, and, as such, fail to include the very important business stakeholders in a client organization. A technology-only approach to MDM results in significant pieces missing from the puzzle, which will impede how well MDM will work for an organization, particularly to produce business value.

These missing pieces relate to people, organizational change and new processes, which in turn often give rise to internal political issues that must be identified and dealt with. For successful MDM initiatives, technology should take a back seat until thorough understanding is mapped out for the people, practices and processes of the organization. Otherwise there will be a serious impairment of the integrated vision of how a master data initiative impacts the organization and the work that is done to achieve business objectives.

A focus solely on technology ensures that the needs and requirements of the business won't be met, and that the MDM initiative will fail to address essential non-technology elements, such as relationships with key people. Business champions, stakeholders and influencers are all critical to the success of any MDM project.

Data governance is another crucial function that aligns the important elements of people, practices and processes with master data management. Again, the plan for data governance must be in place from the beginning of an MDM initiative. It is an essential apparatus for helping to mitigate the many obstacles that can plague MDM.

Third party services providers have a big opportunity for providing MDM services. But such services must include the business and people sides of the story. Often a neutral outside consultancy or service provider can help achieve the organizational change required for MDM to make a difference for businesses. Service provider insight and experience enables the identification of problems from the beginning and provides useful solutions and guidance for clients to mitigate internal politics.

Services providers must be prepared to develop a well-structured MDM vision for clients that aligns to and realistically supports overall corporate strategies, business unit goals and optimal business operations. Such a vision must address not only how business is done today, but what organizations want to accomplish further down the road. A focus on technology alone cannot achieve this sort of visionary work. By providing the full scope of work for MDM initiatives, a service provider will stand well above those who only focus on MDM as a technology "project".

 

Image source: http://www.danpontefract.com/give-piece-a-chance/

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting

 

Meet Your New Cybersecurity Partner: The CFO

 

 

As with other data management and analytics processes, Chief Financial Officers are increasingly becoming an integral part of the management of cybersecurity initiatives. This is primarily due to the huge financial impact that cyber attacks and data breaches can have, seriously threatening the financial health of the organization. CFOs are also beginning to align their oversight of risk and compliance with the proliferation of cyber threats, providing an added imperative for CFO involvement in cybersecurity strategies.

A broad range of organizational information underlies everything that the CFO and his team do on a daily basis, and much of that information is both highly sensitive and crucial to the business strategies of the organization. The CFO must have constant knowledge of where this information resides, who is working with it, and the plans to keep it secure. And now CFOs must have heightened awareness of where cyber threats are coming from and the many ways that intruders can get to that information.

Besides working to ensure the safety of corporate information, CFOs also must assess the risk and potential impact to the organization should a hacker successfully breach security measures. The damage to an organization is not just determined by the direct cost of the cyber attack. As many well-known corporations have demonstrated all too often, there are often long-term negative consequences related to the loss of trust and a tainted brand reputation.

Investments in cybersecurity technology solutions and services are increasing each year. Managed services providers are finding that their clients want and need a partner who combines knowledge and expertise for cybersecurity itself with the implementation of effective technologies that can keep pace with fast-changing methods of attack. And MSPs must understand the responsibilities of a corporate office like the CFO and the mandate for secure corporate financial operations. For MSPs to do well, understanding business objectives, processes and corporate behavior is every bit as important as top-notch technology skills.

Every new technology development translates into new ways for hackers to digitally threaten organizations. The in-depth and broad range of MSP expertise for many technologies and business processes is invaluable for helping CFOs not only grasp the current state of security in their organizations, but better visualize what the future may hold for new kinds of cyber attacks. As organizations adopt new technologies, they should also develop new plans for protecting digital corporate assets.

The future of managed services depends on these new ways of working with an organization, to best support the goals and business outcomes that the organization seeks. MSPs can no longer simply look to traditional IT roles and ways of working. Sophisticated technology and the digital era demand that managed services and solutions always look at the bigger picture and work with more non-IT roles. Innovation calls for new approaches to help organizations attain success.

 

Image source: IBM

 

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting

 

 

Connecting the Cyber-Threat Dots through Big Data

 

The managed security services market has been in play for more than a decade. Not surprisingly, it continues to show vibrant growth, fueled in part by cloud-related factors. Research and Markets, in a January 2015 report, estimated that market growth will run from $14.3 billion in 2014 to $31.9 billion by 2019 (with a CAGR of 17.3%). Growth for security services touches just about every industry, and all sizes of organizations.

Managed services providers are tasked with marrying expertise in cybersecurity and cloud with big data analytics to deliver comprehensive protection and proactive measures to their clients. Proactive cybersecurity services call for the implementation of far-reaching and sophisticated data and event management technologies and services, to improve the aggregation and analysis of security event big data. Managed security services should address the imperative for integrating many disparate sources, including big data sources, to derive event correlation that can be aligned with other analytics to continuously improve alerts, preemptive processes, and real-time reporting.

Today's cyber security threats frequently operate as patterns that usually deviate from the expected behavior for most authorized users or for activity on particular devices or IP addresses. To identify and fight such attacks, security teams need technology services that can find and analyze deviant trends. This is a natural fit for big data mining and analytics combined with state-of-the-art cybersecurity programs.

Machine-generated data provides fertile ground for using big data analytics to root out cyber security threats. Machine data lives in the IT infrastructure: network logs, event logs, firewall and security system data, web logs, email logs – anything and everything operating in the infrastructure. But machine-generated data can be quite problematic for aggregation, data mining and analytics. Such data must be processed very quickly, frequently in real-time and usually exists in large volumes that are continuously proliferating. Machine data sources are quite variable, many of which are multi-structured formats that further challenge data mining efforts.

Once data has been extracted from machine-generated sources it can be enriched with other kinds of data to establish context, and elicit patterns and trends related to cyber attacks. Managed analytics services can take on a forensics quality while searching through data for patterns of irregular or unexpected activity. For instance, realtime endpoint forensic data capture and analysis is on the rise to plug yet another hole in systems infrastructure, in order to block data breach entry points.

Situational awareness is another important aspect of fighting cyber attacks. The overarching value of situational awareness is continuously knowing what is going on around the organization, to "instantly" respond to certain events. Big data analytics strengthen situational awareness primarily through fast real-time assessments that can reduce time to decisions and actions in response to potential threats and certain anomalies. These sorts of analytics also require corroboration from subject matter experts, to help make the best decisions based on the data at hand. And this opens up yet another "cybersecurity frontier" for managed services providers where knowledge and expertise are just as important as optimally running technologies.

 

Image source: platfora.com

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting

 

Moving Target: Cybersecurity and Integrated Managed Services

 

Cybersecurity continues to encompass the pressing need for detailed, broad and varied solution capabilities. Implementing, maintaining and updating comprehensive cybersecurity solutions frequently seem to be daunting tasks for organizations of all sizes. Simplifying cybersecurity solutions is appealing as a beneficial goal -- but the more useful approach right now is to streamline integrated services to handle a broad range of capabilities. Often different industries require specialized security solutions; customization requirements are often part of the mix for many organizations. Managed services providers can cost-effectively handle these sorts of individualized needs as 'business as usual'.

 

Cybersecurity and the Cloud

Like data management and analytics, the cloud impacts cybersecurity solutions on two fronts: the need to provide extensive cybersecurity capabilities for cloud-based applications and services; and the transition to security services and solutions based in the cloud themselves.

A recent article quoted a survey that revealed the problems still challenging organizations for effective cybersecurity to protect work done in the cloud:

 In the face of rapid growth of cloud infrastructure across IT, security has not been able to keep up with the pace of change. Approximately 65 percent of respondents say it takes three months or longer to deploy a new security solution for cloud infrastructure environments. The survey showed that more than three-quarters of survey respondents experience tool fatigue and nearly as many said traditional tools don't scale well in cloud environments…

The survey showed that 77 percent of security leaders say they have too many point solutions to do security well in cloud environments, and 76 percent say they lack visibility into attacks through cloud infrastructure.

 

 

 

 

 

 

 

 

 

 

 

Image source: CDN

 

 

Comprehensive integrated / bundled managed services can contribute greatly to reducing the complexity of end-to-end cybersecurity solutions, while drawing on MSP expertise in multiple technology areas that matter greatly for effective cybersecurity. MSPs need to develop integrated services that address the needs of both cloud and on-premises technologies and work processes, while utilizing elastic methodologies to better defend overall infrastructures from cyber threats.

Cloud-based cybersecurity services align well with the movement of managed services to the cloud, to increase flexibility and reduce costs in ways that pay off for clients. It's important to remember that cybersecurity is very technology-intensive and can be quite complex 'internally'. While MSPs can create streamlined services that work well for clients, the less viable notion right now is the ability to make cybersecurity "simple" (in the way that basic SaaS services are known for). It still takes well-experienced tech experts on the job to make sure everything is continuously updated and working optimally.

 

Continuous Data Management and Analytics

Data is an integral part of effective cybersecurity services, to build consolidated views across all infrastructures (cloud, on-premises) in order to locate and monitor cyber threats as they occur. Data must be analyzed, understood and put to work to continuously anticipate new threats and improve technologies and processes for better protection over time.

Data sources include big data that comes from many areas both inside and outside the firewall. Big data analytics, combined with other data sources, can process: patterns of behavior preceding attacks; attack attributes; and other analyses -- to derive invaluable context to better understand how cybersecurity services should respond to and prevent threats. Predictive analytics processes lead to crafting methods to head off new kinds of attacks. Big data analytics will take MSPs into more 'creative' services that must continuously evolve and be highly responsive to the ultra-fast kaleidoscope of cyber attacks.

 

Through integrated / bundled managed services that encompass cybersecurity, cloud platforms, and data management and analytics, MSPs can take on all aspects of comprehensive cybersecurity solutions to free up in-house IT teams for other important tasks -- particularly for proactive business technology activities that directly support the business endeavors, goals and objectives of the organization.

 

 

This post was brought to you by IBM for MSPs and opinions are my own. To read more on this topic, visit IBM's PivotPoint. Dedicated to providing valuable insight from industry thought leaders, PivotPoint offers expertise to help you develop, differentiate and scale your business.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Managed Services -- Building Real Customer Experiences and Relationships

 

Just like any other business, Managed Services Providers have an important path to follow, to distinctly set their services apart from other MSPs. This path pivots on the commitment to ensuring continuously improved customer and user experiences. High quality experiences for clients comprise a great differentiator in the ever-growing world of new managed services firms and cloud solution vendors. Many services can quickly become commoditized, and too often MSPs resort to "differentiating" on price, a dangerous path that can lead to failure.

As customer and user expectations rise, putting strong focus on their experiences with the MSP organization and its services is a must-have. Customer and user experiences are made up of all interactions across all channels during the entire lifecycle of the client company as an MSP customer, which introduces numerous opportunities to better engage and serve clients.

 

Experiences across the Customer Organization

There are often multiple roles in a single organization that make up the 'customers' and 'users' for technology services. This means very different experiences take place from role to role.

The customer experience frequently aligns with the buyer journey, and post-sale account management for a particular client company. The customer-as-buyer often is a composite of roles: mid-level managers who choose services and technology, VPs who approve purchases, CEOs and CFOs who influence or dictate purchase decisions, and even accounting staff who enact purchase transactions. Defining and engendering excellent experiences for a customer or buyer journey comprised of all of these roles usually requires a lot of work. But this is work that should be done for MSPs to better understand their "customers".

The user experience is much more than utilizing technology services – it's all the interactions that occur during the life cycle of services for a particular set of end users and customers. The term user journey may be the best for describing the ongoing relationship between users and MSPs and the services they provide, including customer support.

Within client organizations, communication disconnects often occur between the customer-as-buyer and end users that can jeopardize the customer relationships that MSPs need to build.

 

Customer Relationships Suffer when Communication Breaks Down

Customer Centricity has taken a look at the communication gaps that can adversely affect customer experiences. The quadrant diagram below spans both the client and MSP organizations to depict how collaboration and communication can fall apart due to disconnects between MSP roles and client roles. Enhanced awareness of these roles should help MSPs consciously improve communications and processes between their organizational roles and client roles, ultimately to ensure positive long-term relationships.

 

            Customer Centricity - Communication Disconnects

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Image & explanation: Customer Centricity

 

Quadrant 1: The (customer) business manager doesn't truly understand the value that is being delivered by the MSP. They only hear reference to the MSP when there is a service problem. When it comes time to reduce expenses, they are hard pressed to justify the line item representing the cost of the MSP.

Quadrant 2: The (MSP) Sales Representative has little to no insight into how the (client) company is "truly" leveraging the product, the benefits that are being realized, the issues being experienced by those using the product and the value being delivered by the "service organization."

Quadrant 3: The customer's end-users and technical contacts play a key role in the effective use and performance of the MSP's service. They are also key in shaping the business manager's perception of the MSP. However, the only time that they discuss the MSP is when there is a problem. They don't discuss all the great things that the MSP has done on a day-in / day-out basis.

Quadrant 4: (MSP) Customer Service / Technical Support typically observe that "all things are well with the customer." This impression, which can often be false, is created because the customer's end-users maintain a courteous working relationship with the MSP's service personnel, to make sure that they can continue to "get things done" when they really need to.

Problems Impact All Quadrants: When a significant business disrupting problem occurs (which it WILL), the customer's end-user remains calm, cool and collected when speaking with the MSP's service team. The service team doesn't find it necessary to escalate awareness of the "incident" to the MSP's Sales Representative or Senior Management because they don't understand the level of impact

 

Building Real Customer Relationships

Many of the "business-as-usual" activities for MSPs are rich opportunities to enhance customer experiences and build long-term relationships. Important areas that make a difference are already in play: basics like consistently meeting or exceeding SLAs, delivering on all promises, usability and ease-of-use, high performance, and so on. This is how trust is built for real relationships.

MSPs can make sure that new client relationships are built on great customer experiences by starting with onboarding processes and/or service activation. MSPs should allocate strong resources that will make these introductory functions engaging, effective and worthwhile. Obviously when done well, a positive tone has been set for ongoing business.

While MSP ecosystems of complementary service providers are extremely beneficial, in most cases one major function should remain under the control of each individual MSP organization: help desk and customer support. Customer support interactions are critical for growing customer relationships and to expand knowledge about different kinds of customers. It's also an opportunity to thoroughly document customer environments and conversations about business needs, to help these customers better utilize all MSP services now and in the future.

The information and data gained from all interactions with customers and users become the fuel for customer-focused analytics that help MSPs learn more about current and potential customers and the sorts of services that they want and need. Such detailed customer intelligence is an invaluable competitive edge.

 

But Who Owns the Customer Experience?

As illustrated by the Customer Centricity communication quadrant, MSPs would be smart to facilitate better communication with – and within – their client companies, to make sure the value of their services is obvious to the relevant customer and user roles.

MSPs must work to improve their own internal collaborations across functions to engender continuous high quality customer and user experiences. MSPs also need to implement ongoing analytics processes to generate and share customer insights across the organization.

Within the MSP organization, the responsibility for nurturing and shepherding ongoing customer experiences belongs to all teams that impact customers. At the core, much of the work should be accomplished by strong, continuously evolving collaborations between functional teams. To keep an agile customer focus, MSPs cannot afford to allow functional silos to exist within their organizations.

In the end, only the customer can own the customer experience – but MSPs have a great deal of power to enhance all customer interactions and the quality of services provided to clients.

 

Customer Image source: hudsonvalleynewsnetwork.com

 

 

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic visit IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Data Savvy Business Roles for Better Analytics Outcomes

 

To put data and analytics to work usually requires multiple roles, not just one, to achieve ongoing value and to align with company functions. No one person can provide all the skills and expertise that are needed. A variety of viewpoints, knowledge and experience need to come into play to get the most from processing and analyzing data – and then to correlate analysis with other information sources and knowledge -- to derive the best overall insight and recommendations.

With the increased interest in big data and advanced analytics, there has been much discussion of the data scientist role. While the data scientist can provide significant value, not all companies will choose to add data scientists. Midmarket companies in particular may not want to invest in this usually expensive professional. But midmarket companies can turn to several existing positions to grow company expertise for data management and analytics. Companies can expand roles for business analysts and LOB users who are more deeply involved in data integration, data quality, analytics and the business processes that create and consume data. Several C-suite roles also need to become proactively data savvy.

Enhancing existing data-related business roles can be a means to better connect technology to business needs and desired outcomes, where many of these roles are strategic and straddle both business and technology activities. Expanded roles for business users are needed to include valuable domain expertise, business insight and real world judgment for analytics and decision-making processes.

Conversely, business users must expand their knowledge of data and analytics to better support their technical partners, so that the most effective activities take place to produce the most useful results. Midmarket companies should seriously invest in developing the skills of multiple roles to better understand data management and analytics – over time this investment will pay off quite well.

In companies where employees understand more about data and analytics, often less time is wasted looking for the right reports or debating whether the right data has been utilized. More time is spent on analyzing the results of analytics and on improving decision-making processes. More employees can be involved in spotting trends and potential problems, to more quickly and proactively manage issues and take advantage of market changes.

The more the consumers of analytics are involved with data management and analytics processes, the better the outcomes. For example, traditionally the strategic executive dashboard has been seen as the "killer app" for most CEOs, where the CEO is usually the 'passive' recipient of intelligence, rather than a participant in its creation. But the growing importance of data and analytics for most companies now calls for CEOs to have a more participatory relationship with the initiatives for generating intelligence and insight, including processes for continuous improvement and constant review of requirements.

 

Image source: http://www.barrysworld.biz/

 

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

How Big Data - and Critical Thinking - Lead to Business Value

                            Originally published on CMSWire

 

There is irony behind a discussion of the business value and metrics related to big data analytics, since there are a lot of people in the business world who still don't understand much about big data, including people who think they do have a handle on big data. Business analysts, IT, Marketing, Business Intelligence, Data Scientists, Upper Management – each of these roles can have a meaningful stake in big data analytics – and it's likely that each role has a different idea about the impact it can have.

Numerous misconceptions about big data – what it is, why it matters – make it difficult for organizations to know what to do with it and to understand the business value that can be acquired. Some organizations have been too quick to declare that big data is only hype or a big flop. Frankly, the same can be said for any business technology initiative - if the right approach and effort aren't undertaken, if participants don't understand why they're working with it, if beneficial strategies aren't in place.

Big data is not just one thing and there's not just one application for it. If big data analytics aren't producing good results, it's not the fault of the big data. Just like working with any data – you have to know what you want to do and why; you have to experiment and learn from the approaches that don't work; you have to adhere to continuous improvement, identify other needed data sources, and so on.

It's a very good idea for organizations to pursue a good understanding of big data and where it might produce value for current circumstances and for future direction. Initially an organization may decide that big data doesn't have a place in current strategies – but strategic thinking should also stretch into when big data analytics should come into play.

 

"Big Data" Isn't Always Big

A good many people think of extremely large datasets and high velocity data when "big data" is mentioned. But there are other kinds of big data sources that provide value to organizations, which means big data comes in disparate shapes and sizes.

Many big data sources are messy and complex, usually dubbed as multi-structured data (some erroneously say non-structured). These are the kinds of data that cannot be processed by traditional technologies and methods – data that doesn't fit neatly into a relational database. Multi-structured data and content comprise highly variable formats and semantics, such as log files, e-mail, social media content. Lots of big data isn't data but text or content that requires the additional dimensions of context and sentiment to attach meaning to the words mined from the original source.  

The largest chunk of big data originates with machine-generated data. This data is continuously produced by an ever-proliferating tsunami of physical objects in every industry that are embedded with sensors interconnected through every kind of network, and that often operate with little or no human touch. In other words, the "Internet of Things". Machine-generated data is definitely big, but also complex and messy.

 

Tools and Techniques – Hard and Soft

The exciting news for organizations is that newer ways to work with difficult data and content text mining continue to appear on the scene. These technologies vary from expensive and complex to lower cost cloud platforms, as well as the Hadoop ecosystem of technologies. The lesser expensive technologies allow for open-ended experimentation with data, content mining and analytics, especially to fine tune approaches and find new and sometimes unexpected sources of insight.

Usable, relevant and insightful analytics output frequently doesn't come from big data alone, which can be fragmented and vague. Big data needs further qualification through the context and relationships that come from core enterprise data, to make sense of what big data may be communicating. Other sources of data and information, including master data, extend the accuracy and value of big data and provide essential context to align big data with guideposts like customer identity, products, and locations or channels of interaction.

Pivotal tools for big data analytics are not always technologies. Critical thinking is still one of the most important tools for any analytics initiatives, and must be undertaken by many roles in an organization.

Critical thinking must be used to determine what business problem you want to solve or which questions you would like to answer. For those involved in big data analytics certain questions should hover in the background: Why are we doing this? What are we trying to achieve?

Further critical thinking is needed to correlate results with other intelligence sources, with the experience of domain experts – and with common sense. Analytics results have to be reconciled with decision making processes and action plans. Then teams must review, improve, change analytics processes – or move on. If the focus is put solely on data, technology and methodology won't magically conjure relevant and accurate insights.

 

Business Value: Subjective and Different for Every Organization

Since a lot of big data work is still in early stages, establishing value is also just beginning. However there are an increasing number of real world successes published by a wide variety of businesses, from large enterprises to midsized and smaller organizations.

Obviously there are hard and soft benefits that can be attained through big data analytics. Trying to calculate an actual dollar value is tricky but not impossible. It requires mapping data and business processes to desired business outcomes, and then measuring the impact or success of the outcomes. But there is also gray area involved in such calculations.

Big data analytics are becoming a significant piece of the business performance puzzle for many organizations. Accenture Research took a look at the impact of analytics (including big data analytics) on high performance companies and found that:

• High performers are five times more likely to aggressively use information and analytics to improve decision making and business performance than lower performers
• Companies that invest heavily in advanced analytical capabilities outperform the S&P 500 on average by 64%
• Companies that invest heavily in developing analytical skills and adopting an analytical mindset recover quicker from economic downturns

 

Selecting Big Data Metrics

As with any strategic initiative, the right metrics are the ones that measure actions and events that relate to key business context and goals. But with the volatility of business and markets, such metrics will need constant assessment for relevance and effectiveness. It's likely that metrics will change continuously, hopefully to reflect actions taken in response to analytics results, evaluations of decision-making processes, the emergence of new markets, and company innovations.

To determine the right metrics, one approach is to start with what you want to achieve (goals, outcomes, effects) and then "reverse engineer" the process of attaining the end results. Identify the parameters of key steps or milestones that can be measured and that map back to big data analytics results. This approach also introduces a discipline of connecting data and analytics to events that matter to the business.

Former Forrester analyst and current IBMer James Kobielus offers these guidelines for measuring the benefits of big data analytics, in this case related to better customer intelligence and improved customer relationships:

Volume-based value: The more comprehensive your 360-degree view of customers and the more historical data you have on them, the more insight you can extract from it all and, all things considered, the better decisions you can make in the process of acquiring, retaining, growing and managing those customer relationships.

Velocity-based value: The more customer data you can ingest rapidly into your big-data platform and the more questions that a user can pose more rapidly against that data (via queries, reports, dashboards, etc.) within a given time period prior, the more likely you are to make the right decision at the right time to achieve your customer relationship management objectives.

Variety-based value: The more varied customer data you have – from the CRM system, social media, call-center logs, etc. – the more nuanced portrait you have on customer profiles, desires and so on, hence the better-informed decisions you can make in engaging with them.

Veracity-based value: The more consolidated, conformed, cleansed, consistent current the data you have on customers, the more likely you are to make the right decisions based on the most accurate data.

 

What Comes Next?

It's still very early in the big data story. Still lots to learn, lots of change coming to how analytics are done. Even for organizations with early starts and lots of resources for big data analytics, a great deal of change will come. And around the world, many organizations have barely begun to tap into big data, if at all.

The tools of big data also must evolve. Yes, a lot of the work of big data analytics is highly technical and mathematical, requiring sophisticated tools, algorithms and the right experts. But more tools need to be developed for the involvement of business roles in analytics processes and for assessing the validity of the results and the right direction for applications.

 

Image source: moniquestoner.com

 

About the author: Julie is an independent consultant and industry analyst for B2B software solutions, providing services to vendors to improve strategies for customers and target markets, products and solutions, and future direction. Julie has expertise in several solution spaces including: data integration and data quality; analytics and BI; business process, workflow and collaboration; digital marketing, WCM and social media; and the pivotal importance of user and customer experiences. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

 

Self-Serve Analytics Really Aren’t DIY

 

Data analytics need to become more accessible to many roles that have not previously been the 'traditional' users of such solutions. To accomplish this, analytics solutions have to change to match the faster pace of business decisions to deliver intelligence when it is needed. And, in this vein, the notion of self-service analytics is a good one: to put increased access to data and intelligence in the hands of business people, without having to constantly involve IT in the process. The solutions that provide self-service analytics are available on-demand, and are oriented to "easier" and faster paths to intelligence results. These solutions also must have sophisticated but cleanly designed UIs, and an approach that guides and educates business users through every step.

Behind the scenes, a lot of heavy lifting has to be done to deliver the right data that is trustworthy and timely to business users for self-service analytics. Instead of direct involvement in various analytics and reporting projects, IT now must support systems that collect, validate and integrate information, frequently from multiple disparate sources, preparing data for 'unknown' business user protocols for accessing the data. IT will have to set up separate analytical processes to continuously test and validate the data, as well as ensure data security when accessed by business users on various devices.

Analytics that benefit business users include:

• Decision-making processes where analytics meet business process management, relying on right-time data, faster data access, and intelligent business rules.
• Predictive analytics where business users explore potential outcomes, such as predictions for future customer behavior based on past behavioral patterns.
• Situational awareness where timeliness trumps 100% reliable data because situations demand quick decisions. Real-time analytics are combined with data mashups from various sources, including pre-prepped data.

All of these trends are highly dependent on reliable data and frequently dependent on business users who are substantial subject matter experts – to ensure that the results or intelligence are realistic, accurate, relevant, and usable. Behind these analytics usually are processes for data integration on-demand. To supply trustworthy data for right-time integrations, data quality is especially important. And IT is an important partner to ensure that consistent data quality is maintained.

The practices and processes for using self-service analytics should be implemented with a business focus from the start. A critical factor for success pivots on IT partnering with business users to achieve optimal and reliable outputs for analytics. IT and tech-savvy power business users must provide training for the other business people in the organization who can gain value from analytics. Such training must address not only how to access the right data and the effective use of self-service applications, but the essential need for critical thinking to determine if the analytics are delivering accurate intelligence.

As self-service analytics evolve, this solution increasingly becomes an important decision-making capability for midsized businesses that is cost-effective and uses IT resources more productively, while allowing business users to step in and do more with the organization's data sources. This is also an opportunity for midsized companies to learn more about the company itself, the way it does business, and its customers and markets. With more people throughout the organization accessing and analyzing data from all over the organization and from external sources, opportunities for new insights are extended greatly, particularly when people in the organization do more cross-functional collaboration working with analytics results.

 

Image source: http://suroscopia.com/do-it-yourself/

 

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

Smart Cities Technology -- Opportunities for Midsized Vendors

 

The term 'smart city' is becoming familiar around the world as urban areas realize that a great deal of benefit can come from connecting physical and digital worlds to better serve the needs of citizens and the administration of the city. A smart city doesn't just result from changes in infrastructure and technologies – it must embody new ways of thinking and planning.

And smart cities aren't just limited to large metropolitan areas. Many midsized cities need smart city technologies to better manage growth and provide quality of life and work for their citizens. Just like large metros, many midsized cities want to ensure the sustainability of resources and services, by using technologies and digital systems to improve operations and efficiencies, and to innovate how cities and citizens interact.

At the core of smart cities is the Internet of Things (IoT) – where many systems and networks interact with each other and often run without human intervention. The Internet of Things for the smart city can include not only municipal systems, but machine-generated data created by citizen-owned items such as home environment management devices, smart appliances and vehicle sensors.

With midsized cities pursuing smart city strategies, opportunities are quite promising for midsized technology companies to help these cities realize their plans. Cutting edge and forward-looking initiatives – using the latest technologies – are often thought to be the domain of large vendors working with large cities. But many midsized and smaller cities need and want to take advantage of similar technologies and capabilities to operate more effectively. Often smaller technology vendors are the better choice as a strategic partner for midsized smart city projects that are frequently implemented incrementally and at lesser scale.

The market for smart city technology solutions is still very young and in formative stages, which means there's plenty of room for midsized companies to enter this solution space. Midsized technology vendors who fully understand the needs, desires and challenges that midsized cities have for smart city initiatives will find success in this market. Part of meeting midsized needs includes in-depth understanding of what will work for the people in these cities. So the right solutions aren't just about technologies but must have a strong focus on the sociology of living in a smart city today and into the future.

 

Midsized Smart Cities Need Big Data Analytics

One important technology solution area for midsized smart cities: big data analytics for the volumes of data created every day by city systems. This data increases exponentially as more smart city technologies are implemented. Technologies for transportation, utilities, communications, and many other aspects of urban life are evolving faster than overall management strategies. All cities lag on fully gaining advantage and innovative benefit from this information.

Big data analytics technologies are continuously evolving to better collect, integrate, process, and analyze this highly disparate information to both improve the systems of the smart city and to apply results to other needs and opportunities. Midsized technology companies have new ways to deliver cost-effective services and solutions through cloud platforms, lower priced data analytics tools, and mobile applications.

As a solution example, midsized technology vendors could provide services for midsized cities for the continuous data analytics needed to govern and improve urban transportation:

• Real-time operational responsiveness
• Personalized interactions with citizens using transportation systems
• Creating new services and changes to policy more quickly and accurately
• Measuring the effectiveness of current decisions and actions

Such services and solutions should be tailored for midsized cities to keep costs down and provide exactly what these cities need for faster, more effective implementations. Understanding future direction will help midsized technology vendors build in the right scalability, usability and interoperability capabilities to keep smart cities continuously smart in real time.

 

 

Image source: archivenue.com

 

This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit  IBM's Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective