Highly Competitive - software industry insights

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

 

Riding the Mobile Data Highway

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Image source:  cisco.com

 

Vendors and services providers for mobility management solutions can overly fixate on the vast array of software platforms and services for "corralling" mobile devices and ensuring secure computing. While these are vital areas, ultimately to enable successful organizations it can be far more important to provide services to amplify the value of what employees are doing with mobile devices. Particularly as businesses have become more mobile, the never-ending ebb and flow of data has made comprehensive data management a mission critical element both for business agility and for guaranteeing trustworthy, useable data.

Mobility adds a distinct flavor to real-time data management, particularly since mobile is a top source of big data. But data by itself – no matter the source – carries no value. Even worse, poorly managed data can cause real harm to organizations. Real value accrues when data sources are streamlined and cleaned up, when the processes that consume and create data are more responsive to business needs, and when business users trust the data they are using for decisions and work requirements.

Obviously business data is everywhere these days, as cloud services and mobile software offerings keep pace with worker preferences for tools. End-to-end task workflows may even take place on multiple platforms, off and on premises. The future of applications for mobile devices is ultimately about providing the workforce with the data they need, when they need it and where they need it. With a growing "mobile first" mindset for workers, organizations must enable as much functionality for their employees as possible while also ensuring data - and corporate - security.

One of the important changes for data management solutions is the demand for many different ways that integration processes can be created and used. Not only do data integration platforms need to be high performance, they must continue to innovate ways to connect to data where it lives, and then perform any number of actions to take data where it is needed. Added to integration and management imperatives is the ongoing battle to eliminate more data silos, to speed up access to the right data from mobile devices.

With 'speed to solution' as a crucial aspect of mobile data management, managed services can play an important role to provide what many organizations need, by applying expertise in mobility and cloud services to 'old school' data management processes and mindsets. True data mobility gives rise to data flows consistently and "effortlessly" (from the user perspective) between any points whether cloud or on premises.

Managed services have a natural fit with the management of the entire life cycle of mobile data and information. The objective is to keep up with changes regarding where data is created and stored, and where it needs to flow, to reach everyone in the organization who can benefit from such data. So mobile data management is as much about continuously creating and maintaining the right business processes as it is about data access and security.

Process-centric initiatives are often optimal approaches for the things that companies want to accomplish. Business processes make it possible to directly connect work to business outcomes that lend themselves to various metrics that provide clarity for business performance. It is through business processes that data can be mapped to business value.

Some of the difficulty for transitioning how enterprises derive value from business processes (and the data running through them) is the fact that most data-related initiatives have been driven by IT instead of by business-oriented roles. To enable better business outcomes, mobile data management initiatives need to bring business and IT teams together as symbiotic partners. Managed services providers can be the game-changing bridge to promote strong relationships between business and IT teams in order to best support a growing mobile workforce.

 

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

 

Achieving Mobile Advantage

 

 

As mobile technologies grow in importance for all businesses, managed mobility services can fill a critical role to help many of those businesses manage, support – and most importantly – leverage mobility for business advantage. Mobile technologies and services will continue rapid change and variation, making it difficult for many businesses just to keep up – let alone figure out how to use mobile in different aspects of the business. The tight relationship of mobile and cloud has helped the cost-effective deployment of managed services that should be attractive to businesses.

Mobility challenges for organizations include strategically connecting mobile endeavors to the business, ensuring integrated implementations of diverse mobile technologies, keeping pace with employee-driven adoption of new apps and devices, and attacking dynamically growing security issues. The bottom-line challenge is securing the realization of enhanced employee and business productivity that positively impact business outcomes. Mobile platforms offer expanded opportunities for timely and effective workforce collaboration and knowledge sharing but organizations need serious help to make it happen.

 

A Healthy Market with Growing Pains

Recent reports from market research firms call out strong market outlooks for managed mobility services – although "volatility" in this market should be expected due to the diverse and constant proliferation of new devices, apps and services, and the growing complexity of mobility initiatives. Many businesses are still wrestling with the management of basic mobility, and have yet to take on larger efforts to gain more direct and measurable business value from mobile.

MarketsandMarkets has published that the managed mobility services market "could grow from $2.92 billion in 2014 to a staggering $11.05 billion by 2019, at a Compound Annual Growth Rate (CAGR) of 30.5%". Analysts at TechNavio have shared a forecast that shows growth of the global market at a CAGR of 32.71 percent for the cumulative period of 2014-2019.

 

Advantages of Mobility Services

Managed services providers can deliver and support many aspects of mobile initiatives:

• Consultative third-party assessment of mobility related to the organization's objectives and needs
• Platform-agnostic approach to services, as well as expert support to develop, deploy, and manage mobility initiatives (devices, apps, services, processes,etc.)
• Unified deployment, process creation and management across multiple mobile platforms
• Help desk services and employee training
• Monitoring and analytics to generate intelligence to help manage performance and cost

When businesses partner with providers of managed mobility services, decisions can become less reactionary and more proactive in terms of the many roles mobile can play in an organization -- to work towards the creation of long-term mobile strategies. Managed services providers as mobile experts can continuously contribute updated information on future trends, new services, and additional ways organizations can benefit from mobility. Such support is much needed by many organizations that lack the time and resources to truly take advantage of and control what mobility has to offer. The key for success for managed services providers is to offer highly beneficial and differentiated services that show clear value. Mobile ecosystems of value-added services are on the rise and should be of interest to services providers.

 

Image source: computerbilities.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 – Strategies for B2B Software Solutions: Working from the Customer Perspective

 

Finding Meaning in Big Data Through Metadata

 

 

With the growing interest in big data analytics combined with the shortage of affordable experts and skilled practitioners for deploying and managing big data initiatives, many organizations are looking to managed services as a way to fill the need. More MSPs continue to develop sophisticated services for big data processing, along with all other aspects of data management and analytics.

Big data processing can function well as cloud-based, end-to-end managed services that integrate bi-directionally with on premises data centers and systems. End-to-end services can include streaming data flows, data management activities like data governance, and data visualization and analytics. Big data analytics on cloud infrastructures require top notch management of security and compliance, areas that MSPs are currently improving anyway to proactively move into new technology areas in demand for many companies.

Remember: big data doesn't just refer to high volume or fast-moving data that is important to large corporations. More companies of all kinds need to extract value from a wide variety of data sources – often these sources are difficult and messy for traditional data processing. These sources are also part of the "big data" analytics story and often yield valuable insight. "Messy" data sources include: multi-structured or unstructured data with highly variable formats and semantics like social media content, log files, and e-mail; and machine-generated data from medical devices, industry sensors, automated machinery and systems, and GPS.

 

Metadata Management Services

An important must-have capability for big data processing services is comprehensive metadata management. Metadata provides information about a data item, such as product, that uniquely describes that item. Product metadata can include product ID, product category, supplier ID, size or dimensions, and so on. A field like product ID is also a means for linking to other data sources, for integration purposes. Through metadata descriptors, we can talk about data items in common terms, outside the actual item, and take advantage of metadata to integrate and better understand disparate data sources.

Metadata management is one of the key areas of comprehensive information and data governance. Agile data governance tools are in demand and all sorts of data management vendors are chasing this business. As MSPs expand their presence in data management and analytics services, metadata management and data governance should be of great interest to them. Hopefully the availability of managed services in these areas will make comprehensive data management more affordable and available to organizations of all sizes.

 

What Metadata Does for Big Data Analytics

Metadata is not just about data integration and enterprise data warehouses; for other enterprise needs, metadata helps find data during data discovery, and points the way to interpret and use data correctly. Metadata can greatly streamline and enhance processes to collect, integrate, and analyze big data sources. Working with messy big data like multi-structured sources means that metadata is critical to understanding this data and to connect it to other data sources. Multi-structured data comes with many difficulties – information can be mined, but for it to have meaning and value, attributes like sentiment, purpose and context must be determined as well, and correlated with data sources for customers, products, and so on.

Traditional data sources, such as relational data, provide plenty of logical structure through more easily obtained metadata. However, big data often does not carry a lot of 'native' metadata, so metadata from external sources is essential to unlock meaning. Big data may have to go through certain analytics processes to construct the beginnings of metadata. Then this metadata is correlated with the metadata from other data sources to derive the most useful logical model. As big data processing evolves, new types of metadata may arise to meet the special circumstances of different kinds of big data.

 

Image source: Princeton Tutoring

 

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

 

Data Profiling Services: Putting Business Data to Work

 

 

Image source: itbusiness.ca

 

Within each business there are many people who possess the knowledge of how data needs to be used for business processes and particularly for how the right data can improve business agility, revenue growth and help meet customer expectations. Such employees are invaluable for examining business data: first, to determine its value in context, and second, to understand how it contributes to intelligence being gathered for the organization. The need to analyze data is at the foundation of every effective data management strategy, whether the analysis is handled from the business perspective or the technology side of the equation.

To connect savvy business people to the data they need requires the right technology tools. One tool that is still not easily made available to business users is data profiling. Data profiling is the process of analyzing data to determine things like its structure and meaning. Users set up a variety of methods and processes to reveal patterns of data usage, metadata matches, value completeness, degree of data quality, and so on.

Unfortunately data profiling has primarily been a tool for IT and data management teams working with data repositories and the systems that produce and consume data. But data profiling should be an important tool for business users to gain full value from data assets. Aided by data profiling, business users can quickly identify inconsistencies and problems in the data, before it is used for intelligence and decision-making purposes. They can also continuously learn more about different data sets to understand how such data fits business needs. Until data is understood in relation to various systems and processes, it's not possible to fully and reliably use the right data for the business.

Here's where managed technology services can put an interesting spin on connecting business users to tools like data profiling that have mostly been the domain of IT teams. Providers can package business user access into managed services for data management, to open up easy usage of data profiling for business purposes. In addition, providers that bring a specific industry focus for such managed services create even greater value for the business.

For example, there are services firms that consult with corporate legal departments and offer managed services packages that blend data management expertise with legal and regulatory expertise. Such managed services include pre-built data profiling services for areas such as risk management and eDiscovery for litigation and compliance. Many organizations are overwhelmed by the complexity and volumes of data that must be monitored and assessed, particularly in light of the challenges of meeting compliance and eDiscovery deadlines. Managed services, backed by industry expertise, can provide a sustainable and scalable solution while reducing the demand on internal resources.

But these firms usually provide these services only for IT and data management teams. Such managed services could and should include expanded data profiling packages tailored for business users who are less technical but are business data savvy. Data profiling processes are almost always recurring, to support continuous efforts to gain more value from data and to ensure that data quality is reliable; as such, managed services have a 'natural' fit with the creation of tailored data management services for business users.

 

 

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

 

 

Cloud Expands Value of Purpose-Built Data Management Services

 

The ongoing value of purpose-built technology products stems from the savings in productivity and cost that come from delivering only the features and functions needed to get a certain job done. Purpose-built software solutions are an opportunity for cloud service providers to deliver well-differentiated services that are built on domain expertise for explicit problems related to specific industry scenarios. This approach often results in a strong competitive market presence for the cloud services provider.

As data management platforms and solutions continue to emerge as cloud services, purpose-built data management capabilities can be an important area both for the providers and consumers of cloud services. Data integration, in particular, has taken on immense importance due to the continuously proliferating data silos in the cloud and the need to integrate cloud data with on-premises systems. Purpose-built data integration is usually designed to meet a well-defined business need, with specific capabilities bundled in the solution, frequently delivered as an appliance.

Taking the purpose-built route for data integration services aligns with top advantages of cloud computing:

• Up and running quickly to handle integration tasks
• Enterprise-quality capabilities available to organizations of all sizes
• Always-on availability from any device or location
• The most current capabilities, without the need to purchase new licenses or new versions
• Pay-as-you-go, pay for only what is used

There is clear advantage for cloud services that support specific point-to-point integrations that can be performed easily and inexpensively, enabling less-technical business users to handle certain integrations between applications. A common integration need involves CRM and ERP systems, such as data flows between Salesforce.com and SAP. Services are built for focused use cases where business users do not have to make lots of decisions about how the integrations will be accomplished. Wizards are often employed to walk users through the process. However, business users must have good knowledge of the source and target datasets, and basic understanding of the data integration task, to work confidently and accurately through the purpose-built approach.

Cloud services for integration usually include data replication, migration, and synchronizations for customer data. Typically integrations involve certain cloud / SaaS applications as "pre-set" sources and targets, as well as on-premises systems. Data integration cloud services should also include data quality tools to involve business users in this important task.

Data integration solutions that can handle data located anywhere, as well as security and on-demand considerations, are important for connecting cloud services to on-premises systems. For many companies, the business technology that underpins those organizations is going through a transition from an infrastructure of all systems inside the firewall to more of a "hybrid" infrastructure comprised of part cloud-based services and part on-premises.

However, not all integration processes can be reduced to "simplicity". That's why data integration is still a tough nut to crack. In these situations, integrations will require the help of IT teams, consultants, and/or managed services using more sophisticated approaches. For organizations with broad needs, purpose-built data integration quickly becomes less useful as business needs change and the types of projects fluctuate. For such needs, a comprehensive and flexible data integration platform usually provides the best approach to handle current projects and to scale to address future integration challenges.

 

 

 

 

 

 

 

 

 

 

 

 

Image source: pomeroy.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

 

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