Practical Process: The Primacy of Process

I believe that the primary focus of every organization should be the understanding, management, and continual improvement of the business processes by which customer value is created, accumulated, and delivered. Not the only focus, of course, but by far the most important. This is the essential message of process-based management and once that message is heard, the need for both process management and process improvement is obvious, urgent, and compelling. Let me explain what I mean by, and why I believe in, the ‘primacy of process`. This Column is deliberately short (if I had been in a hurry I would have written a longer one!) because I would like to find a way to succinctly define the key message of process-based management. Have I achieved that? Please let me know.

Process?

Why “process”? The simple view of a process is that it is a collection of cross-functional activities that transforms one or more inputs into one or more outputs. I also include all of the resources, people, systems, infrastructure, policies, regulations—everything that is required to execute and manage the process. Processes are important because this is how organizations get work done.

Primacy?

Why “prime”? Cross-functional business processes are the only way organizations can deliver value to customers and other stakeholders. By themselves, the separate functional areas of an organization cannot deliver value to external parties. An organization`s resources are managed ‘vertically` via the organization chart. Value is created, accumulated, and delivered ‘horizontally` across the organization chart.

It follows that an organization executes its strategic intent via its business processes. The sequence from strategy to execution is shown in the breakout box, From Strategy to Execution.

The only way an organization is able to exchange value with its customers and the way in which it executes its strategy—sounds prime to me!

Implications

If we accept that business processes are the value pathways, as well as the way organizational strategy is operationalized, where does that lead us? The inescapable conclusion must be that the starting point for effective organizational management is to understand, manage, and optimize those business processes. Without a proactive focus on business processes, organizational performance cannot be optimized and strategy cannot be effectively executed.

If value is created, accumulated, and delivered across the organization, what measurements of performance are made in that direction? Do we know if those cross-functional processes are working well? Do we know what “working well” would mean? Do we know where the performance gaps are and if anyone doing anything about them?

If value is created, accumulated, and delivered across the organization, who is in charge of that? The organization chart is silent on such cross-functional matters. Sure, we can say that the CEO is responsible for everything, but she knew that and it doesn`t help. Can it really be a good idea that the path through which we exchange value with our customers is not managed?

Relationship to Business/Enterprise Architecture

I`ve said that process performance should be the primary focus of an organization, but not the only focus. There are many other aspects and the positioning of the many other architectural elements naturally arises.

There are many “architecture” types. As well as business architectures and enterprise architectures, there are architectures that address IT, information, capabilities, services, systems, rules, applications, organizations, and people. This Column does not seek to either explain or reconcile the many views of business or enterprise architecture, but it does have a particular architectural stance—the primacy of process—and this requires that processes be the focal point of any architectural view. All of the common architectural perspectives are valid[1], but the process context is fundamental and each other perspective has a direct relationship to it. While other constructs are useful and often vital, particularly to guide information systems, the tendency to define ‘every` object and show the relationships to ‘every other` object via abstract diagrams, may be of limited benefit to managers.

Accepting the ‘primacy of process` principle, and starting with the process architecture, allows an organization to focus on the purpose and performance of the value pathways. Other architectural elements can be added when they are be shown to add value, when they are an aid to management and not a further complication.

Conclusions

Management needs its own disruption. A practical and pragmatic approach to understanding the organization as a system for value creation, accumulation, and delivery is required. Clear links between strategy and process execution must be defined. Mechanisms for identifying, managing, and improving process performance must be realized.

Organizations must reimagine their operations as value creation and delivery flows.

In Practice…

There are many things you might do in response to the issues discussed in this Column. Here are three practical steps you might consider doing now to get started on the creation of sustainable process-based management and process improvement.

Discover the value pathways

Document the process architecture for your organization. Find the strategy statements and determine who are the customers and other stakeholders and your organization`s value proposition for them. These are likely your core highest level processes. Decompose those processes down a level or two.

Agree on performance targets

If those processes were working as well as the key stakeholders would like them to, what would they be doing? How would you know? Define and document the critical few performance measures.

Mind the gaps

Capture the performance data and make evidence-based decisions about what to do for any performance gaps.

[1] Although I still struggle to see how ‘capabilities` add value that is not already provided by ‘processes`, but we`ll leave that for another day

SOURCE: BP Trends

Overcoming Legacy Processes to Achieve Big Data Success

For large companies with legacy infrastructure, changing to a Big Data focus isn’t easy — but AMEX proves it’s possible.

For large companies saddled with complex legacy environments that were developed over many decades, finding the opportunity for disruption can
be a daunting challenge.

Unlike new economy firms, which have had the benefit of being able to
build their businesses and infrastructure from scratch in a “green-field” environment, most large corporations are saddled with disparate and
fragmented operational and analytical environments and processes,
characterized by business and data silos that limit the ability to operate
with agility, flexibility, and insight across customers and lines of business.

While institutional reputation, customer reach, and operational scale
provide many advantages to large corporations, these larger firms cansometimes be challenged when it comes to innovation and
responsiveness. Yet it’s still these large corporations that continue to be
the bulwark of the economy, accounting for the bulk of business and
consumer transactional activity. Now, Big Data approaches that were
developed by the new economy firms, which enabled the flexibility and
rapid growth of these firms, are being adopted by mainstream
corporations to overcome legacy challenges and introduce greater
corporate agility and speed.

A 2014 survey conducted by NewVantage Partners of 125 senior
corporate executives, representing 59 Fortune 1000 companies, showed
that more than two-thirds of executives reported that their organization
had a Big Data initiative in production. These same executives reported
that investments in Big Data are projected to grow dramatically in the
coming years — with 75% of executives reporting investments greater
than $10 million, and 28% reporting investments exceeding $50 million
by 2017.

For these corporate giants, operational process optimization can have
a huge business impact.

One such firm that’s benefiting from new Big Data approaches is
the financial services giant American Express (AMEX), which is
harnessing the power of its data to innovate and to streamline complex
operational processes. AMEX has opened its own new technology hub
in Palo Alto, led by former Google and PayPal executive Nik Sathe.
The new tech center “will focus on innovations in Big Data, cloud
computing, and mobile infrastructure,” according to AMEX CIO Marc
Gordon.

AMEX has taken steps to optimize and streamline its operational and
data processes end-to-end by migrating many traditional processes
from legacy mainframe environments to Big Data processing
environments, resulting in dramatic improvements in speed and
performance, significant reductions in cost, and notable increases in
responsiveness to customer needs.

Ash Gupta, chief risk officer and president for Risk and Information
Management for AMEX, comments, “Fact-based analytics have always
been in our company’s DNA, yet I have never seen such an analytical
leap as the one we have made with Big Data.” He cites several areas
where AMEX is focusing its Big Data efforts, notably in addressing
service excellence, generating billings and receivables growth, and risk management. A few examples:

- Using Big Data platforms, AMEX has been able to dramatically reduce
the time it takes to process the thousands of variables and billions of calculations needed to match customer card offers to card member
interests. It previously took years of processing time just to sift through
these massive volumes of data, but now it can be done in hours.

- AMEX has made similar strides in connecting merchants to card
members on personalized offers, taking a 3-day process and reducing
it to 20 minutes on their Big Data platform.

- American Express uses Big Data analytics to detect and prevent fraud
in milliseconds and with greater precision, using machine-learning
models that leverage billions of historical transactions across millions
of card members, resulting in instant fraud alerts for customer protection.

Leading companies can learn from the example of AMEX and other
mainstream corporations that are adopting Big Data solutions and
approaches with success. Here are a few suggestions:

- Assess opportunities to migrate operational business processes from
mainframe environments to faster and cheaper Big Data approaches.

- Create a Big Data Center of Excellence or Analytical Sandbox as a
testing ground to identify suitable applications for Big Data.

- Adopt a “data first” approach that focuses on rapid identification of
data insights, rather than investing in large data warehouse initiatives.

- Develop a “culture of data” staffed by a next-generation of analytics
professional, whom are conversant in new Big Data approaches and
analytics techniques.

Mainstream companies will drive the future of Big Data investment. But
they must demonstrate a willingness and flexibility to tackle their
complex legacy environments and data infrastructure. For those firms
that make the commitment, as evidenced by the example of American
Express, the payback is likely to be high. Their ability to compete for
customer success in the coming years may depend on it.

SOURCE: MIT Sloan Management Review

What is Kanban and How Did It Change Management?

If you’ve shopped for project management or enterprise resource planning (ERP) software recently, you’ve likely come across the term ‘Kanban.’ If you’re like most software buyers, you might not be familiar with what that means. Below, we explain the history and business uses of this system.

What is Kanban and Where Did It Come From?

Kanban is a Japanese word that literally means “card” or “billboard.” In terms of project management, Kanban is a way of visually organizing production using cards, each of which represents a task or step in the production process. The system was born in the late 1940’s by Toyota engineers who drew inspiration from the “just-in-time” delivery model of supermarkets. Instead of ordering product to restock shelves based upon vendor availability, supermarket clerks ordered based upon the current store inventory. The engineers used this idea as a model to develop a manufacturing process that relied on matching inventory with demand in order to increase both quality and throughput. The result? Kanban.

On the manufacturing floor, workers would use a Kanban, or card, to represent steps in the manufacturing process. Adjacent up-and-downstream workstations communicated with one another via the Kanban cards. A container at each station would contain a Kanban that, when received, authorized the station to produce parts or ship the full container to the next workstation. The visual nature of using a card or billboard allowed teams to better communicate with one another, which maximized productivity. The result? Toyota became the largest, most profitable auto manufacturer in the world.

Kanban Goes Mainstream

Kanban was only used for manufacturing until the early 2000s, when David J. Anderson, Corey Ladas, and others developed the Kanban method for software development. It was centered around the idea of making incremental changes to processes and systems, and could be used by corporations in any industry, not just manufacturing. It is a “pull” system, where work in progress is limited in order to reveal bottlenecks that prevent supply from matching demand.

Of course, the introduction of the Kanban manufacturing process to software development resulted in the development of e-Kanban, or electronic Kanban systems. Instead of physical representations of work, e-Kanban systems, like those found in Kanban-based project management and ERP software, use electronic cards that may or may not contain barcodes, attachments, or other electronic messages.

Whether used for development or manufacturing, Kanban has six rules, developed by Toyota, that help ensure a successful implementation.

1. Downstream processes use items only in amounts specified by the Kanban card.
2. Upstream processes produce items only in amounts specified by the Kanban card.
3. Nothing is made, moved, or altered without a corresponding Kanban card.
4. If an item is produced or shipped, it must have a corresponding Kanban card.
5. Errors, defects, or shortages are never sent downstream
6. The total number of Kanban cards are limited to reduce inventory or work-in-progress and reveal bottlenecks or other problems.

Do You Need Kanban?

While the benefits of Kanban/Lean processes to manufacturing and development are relatively well-known, a Kanban system can be used with great effect in a myriad of industries. Any team that generates a product can use Kanban. We even use Kanban software on several teams here at TechnologyAdvice. While Kanban takes some getting used to when transferring from a traditional waterfall system, once your team becomes more comfortable with the process it will help reduce lead times, and increase team communication and output. 

To find out more about Kanban-based project management and enterprise resource planning systems, call, click, or email one of our Technology Advisors for a no-cost, no-obligation consultation on how Kanban can benefit your business.

SOURCE: TechnologyAdvice

Are Your Operations Doing the Right Things, at the Right Time, in the Right Way?

Companies have focused on efficiency for over 15 years now.  But are they making the processes effective?

For this column, let’s consider efficiency to be doing work as quickly as possible with a low error rate.  Then let’s consider effectiveness as eliminating all work that is not really necessary – doing the right things, at the right time, and in the right way.

Too many BPM efforts however, are so narrowly focused on cost takeout that they miss the fundamental question of need – “do we need to do this in the first place?”  Of course, the answer to this question must be backed by the answer to the question “why?”  Anything that falls outside of this need is extra and is a candidate for elimination.  

Not surprisingly, doing this before the team considers efficiency will save a lot of time and cost.  Why improve things you don’t need to do?  But it will also leave holes that will need to be removed.

Effectiveness

Efficiency lives in process – no real news there.  But where does effectiveness live?

I submit that effectiveness lives in fundamental “need”.  What do you really need to do?  The framework for identifying this “need” and thus effectiveness is set by the company’s strategy and given context in the company operating model.  Anything beyond this fundamental or basic need in producing the service or product is extra.   What is left will require specialized support activities to knit together – but that can be managed through careful action and task justification.

So we can find what we need to do to be effective, and we can then make that efficient.  But before we make anything efficient we need to first look at how we can guide work so that the right answers, components, business operations, and assemblies come together to deliver the service or product component.  We need to find a way that guarantees the right logic is considered and the right questions are asked to comply with legislative and operational requirements while constantly being guided to the next step in the work.  

This requirement for guidance is the realm of business, manufacturing, technical, and other rules.  The fact is that rules define the “what” of the business - while process defines the “how”.  If we consider process to be the life blood of the company, carrying the components that are needed to produce something and thus keep the company operating, we can consider rules to be the “brains of the outfit”.  They direct everything and tell us how things need to be done.  Rules thus set the operating framework and are both interpreted and supported by standards to set performance measurement limits on the work.  Taken together, rules and their supporting standards, define how operational effectiveness will be viewed – within the context of company strategy.

Finding rules

Rules are everywhere.  Most are, however, not written and those that are, are seldom up to date.  So where do you start?  The following is one approach – it is not the only one however.  But, it works.  Let’s start with effectiveness in any process, business unit’s work flow, or the applications that support the work.

The first step is the archeological dig through the company dustbins – existing documentation.  Procedure manuals, HR rules, financial rules, sales rules, and compliance rules should be collected and the rules vetted.  That is the foundation – you may very well find rule conflicts, out of date rules, and rules that no one knew existed.  Vetting them is tedious and will probably be resisted.  But it is critical and needs to be done to create a foundation for the operation
.  
These rule collections will first focus on the business operation.  IT should next add its technology related rules – the Do’s and Don’ts that must be considered in any business activity support design.  To make this collection useful, care will need to be taken in finding a rules “engine” or storage application, coding the rules for electronic storage, and defining an indexing structure that will make it easy to find and thus modify, version, or reuse any rule.

With this foundation in place, it will be time to look at the business operation itself.

Any look at what makes a company effective must really start with strategy outcome and work backward.  The outcome description is where you first know how goals will be delivered and where the business model must be changed to support the strategy.  All capabilities will be identified and defined as part of the outcome definition. The capabilities then tie to process and a walk back up the process is a look at what it takes to build the product or services.  

The activity in a business unit represents the work that actually creates the components and does the construction of a solution.  The rules in the company guide every action, every decision, and every step of the work.  But when many teams look at a BPM project, they focus on the activity, not the rules that help identify if the activity is even needed – if the activity is not important or complex enough to require guidance the team really must ask why it exists. Similarly, if the rules do not support the actual work, why do they exist?  The two are partners in a corporate dance that determines what should be done and how it should be done.
Of course, if the rules are overly complex, the team will need to see if they can be streamlined.

In addition to the “easy to find” rules, teams will find other rules (often the most important ones) in the heads of the staff.  They do the work and apply rules constantly – some they make up to control new work.  Many of these unwritten rules are not found anywhere in the “documented” and now vetted rules.  In some cases, these rules may be imbedded in applications that the staff has learned about over time through use.  In other cases, the rules are work-around rules that have had to be informally created to get past changes in the operation, in law, and in application systems.  I call these “white space” rules.  Only the people doing the work of getting around things know these rules. 

The fact is that these rules will tell the team how the activity really works.  Comparing these rules with the ones you have already documented will point out redundancy, differences, conflicts, and erroneous activity.  In some cases I have found old rules that no one knew were still in use had to be changed immediately to comply with current legal requirements.

Rules
As noted rules are everywhere and at many times are the hidden mandate for any activity.

But today we see that many from the BPM world do not pay enough attention to business rules and many from the BPMS world look at rules from a technical perspective – aligning them to the applications in the project or solution.  In both cases, these rules are generally poorly defined and unavailable for general use.  They are also not really adequately shared between the business and IT sides in creating a new business solution.

The need to address this is critical and cannot be underemphasized – you must get a handle on your company’s business, technical, production, customer, compliance, and financial rules.  In healthcare, there are even more rule categories on the clinical side of services.  Without a detailed understanding of applicable rules and immediate access to them, it is difficult to look at change and anticipate impact.  

However, what we should do with rules is a matter of opinion.  The same is true in determining what we need to do.  The fact is that both considerations are often debatable.  Today, I find that rules are often one of the orphans of the modern IT and business operation activities.  Both IT and the business seem to have divided business transformation and IT solution development and in many ways are recreating the divide of the past 50 years.

The problem is that many teams really don’t look closely into the rules in a business or IT operation and ask the probing fundamental questions.  These include:

1.    Is the procedure manual up to date?  How can we bring it up to date?
2.    Do we really know all the rules that guide the work or the creation of IT support?
3.    Are the company rules written down?  Have they been reviewed by business area managers?
4.    Has legal and finance reviewed the rules and vetted them?
5.    Are the rules stored in a rules library?  How are the rules organized?  Are they linked to business activity and are they easy to find in the rules library?
6.    Do we know every place in the business and every application that uses each rule?
7.    Are all compliance related rules defined and aligned to the business activity work? – Are we in compliance with all important state, federal, and international (for the countries we do business with) rules?
8.    Have we been fined for reporting violations in the past?  What did we do about that?

Why is this important?
Compliance, HR, legal, and financial rules represent laws and require that you follow them or risk being fined and maybe go out of business.  Other rules determine how things will be done and provide order out of what would be operational chaos.  The fact is that rules are the logic of the company and project the beliefs and culture that executive management wants to infuse into the workforce.

In the context of this column, rules direct how all work is done and how all decisions are made.  They provide the framework for business activity and they guide workers and managers down a specific path in interacting with customers, building product, and running the business.

The simple fact is that without rules, even if they are not written, everyone in any business would be able to do their work in whatever way they thought right.  Chaos would reign and the company could not compete.  So why are rules important?  They simply guide all work and compliance with appropriate laws.  They keep you in business.

So who owns rules?

Who is responsible for finding them, defining them, and then both managing them and changes to them?  Not a small task.  There are thousands of rules in any mid-sized company and a lot more in a large company.  There are, however, no standards or norms for rule ownership that I am aware of.

There is also seldom anyone who has the authority to, or the responsibility to, collect, vet, index, and store rules.  This is one of the big problems that hampers effective and efficient business operation and slows any type of business improvement or transformation.

But the fact that there are rules everywhere and that there are business activity rules, decision rules, compliance rules, HR rules, finance rules, legal rules, social rules, policy rules, technology rules, data rules and on and on, makes finding, updating, and controlling their use a difficult task.  So who is responsible for rules?  This takes commitment from a person like the COO or the CIO and it must be considered to be a strategic necessity.  If not, it will not be funded.
The fact is that rules are a company asset!  

Rules allow the company to run with some consistency and efficiency – they help make certain work get done the right way.  They support strategy and compliance with laws.  They are the combined intelligent evolution of the company and how it works – gathered over all the years that the company has been in business.  They are one of the big things that provide any company with a competitive edge.

The fact is that whoever can change the fastest with the lowest risk will have a real advantage.  Those who can include access to all relevant information for these rapid changes will have an even greater advantage.  Arguably an advantage that combines rapid improvements to both effectiveness and efficiency offers a timing and cost of change advantage, and builds flexibility into the operation.

Because a company’s rules define and support this competitive differentiation, they should be considered to be undiscovered company assets.  Many will represent trade secrets and some may be based on patents and proprietary thought leadership.  All of which are company assets.

Formal rule definitions are also important in operations and business interruption – Disaster and Recovery.  If they are not known and if known but not formal, a remote hot site for Disaster Recovery is not really much use in many companies.  The computers will work in the hot site, but operations will be hurt.

Note:  A Disaster Recovery hot site is a remote location that has fully operational computers and work space running 365 days a year.

A looming catastrophe

Companies are about to lose many of their senior workers due to aging.  Without a clearly defined set of integrated rules that can be easily found we can expect the millennials who replace these workers to make procedure and execution errors – the infamous human errors that people are writing about. It is easy to predict serious competitive problems for companies that do not take the time or make the investment to create this foundation for understanding the business and how it functions.  This is not only a business problem.  It is also an IT problem, a manufacturing problem, and a problem in every corner of the business.

Tight budgets have caused cutbacks in the way BPM and BPMS groups look at creating solutions and in the move to integrating components into end to end processes for modernization.  But the market is opening and competition is heating up.  Given the changes in technology, business management, and the approaches to business streamlining, it is clear that some will leverage this emerging technology and some will not.  However, for those that do, change will have removed a major anchor that is slowing the company’s ability to adjust and evolve.  

The purpose of this column is to encourage BPM practitioners to increase their emphasis on identifying and defining business rules and in applying the understanding this discovery process will provide to looking at what rules and work are really necessary to start with.  From that point the traditional look at efficiency can provide even better results.

SOURCE: processexcellencenetwork

How big data analytics tools can help your organization

The analytics process, including the deployment and use of big data analytics tools, can help companies improve operational efficiency, drive new revenue and gain competitive advantages over business rivals. But there are different types of analytics applications to consider. For example,descriptive analytics focuses on describing something that has already happened, as well as suggesting its root causes. Descriptive analytics, which remains the lion's share of the analysis performed, typically hinges on basic querying, reporting and visualization of historical data.

Alternatively, more complex predictive and prescriptive modeling can help companies anticipate business opportunities and make decisions that affect profits in areas such as targeting marketing campaigns, reducing customer churn and avoiding equipment failures. With predictive analytics, historical data sets are mined for patterns indicative of future situations and behaviors, while prescriptive analytics subsumes the results of predictive analytics to suggest actions that will best take advantage of the predicted scenarios.

In many environments, the processing and data storage demands of advanced analytics applications have limited their adoption -- but those barriers are beginning to fall. The growing availability of big data platforms and big data analytics tools has enabled environments in which predictive and prescriptive analytics applications can scale to handle massive data volumes originating from a wide variety of sources.

What does big data analytics mean?

In essence, big data analytics tools are software products that support predictive and prescriptive analytics applications running on big data computing platforms -- typically, parallel processing systems based on clusters of commodity servers, scalable distributed storage and technologies such as Hadoop and NoSQL databases. The tools are designed to enable users to rapidly analyze large amounts of data, often within a real-time window.

In addition, big data analytics tools provide the framework for using data mining techniques to analyze data, discover patterns, propose analytical models to recognize and react to identified patterns, and then enhance the performance of business processes by embedding the analytical models within the corresponding operational applications. For example, massive amounts of shipping delivery data, streaming traffic data, streaming weather data and historical vendor performance data can be analyzed to devise a model for optimal selection of shipping subcontractors within geographic regions to limit the risks of late delivery or damaged goods.

Big data analytics tools can ingest a wide variety of data types: structured data with defined and consistent fields, such as transaction data stored in relational databases; semi-structured data, such as Web server or mobile application log files; and unstructured data, encompassing things like text files, documents, emails, text messages and social media posts.

Powering analytics: Inside big data and advanced analytics tools

A Google search for big data analytics yields a long list of vendors. However, many of these vendors provide big data platforms and tools that support the analytics process -- for example, data integration, data preparation and other types of data management software. We focus on tools that meet the following criteria:

• They provide the analyst with advanced analytics algorithms and models.
• They're engineered to run on big data platforms such as Hadoop or specialty high-performance analytics systems.
• They're easily adaptable to use structured and unstructured data from multiple sources.
• Their performance is capable of scaling as more data is incorporated into analytical models.
• Their analytical models can be or already are integrated with data visualization and presentation tools.
• They can easily be integrated with other technologies.

In addition, the tools must incorporate essential characteristics and include integrated algorithms and methods supporting the typical suite of data mining techniques, including (but not limited to):

• Clustering and segmentation, which divides a large collection of entities into smaller groups that exhibit some (potentially unanticipated) similarities. An example is analyzing a collection of customers to differentiate smaller segments for targeted marketing.
• Classification, which is a process of organizing data into predefined classes based on attributes that are either pre-selected by an analyst or identified as a result of a clustering model. An example is using the segmentation model to determine into which segment a new customer would be categorized.
• Regression, which is used to discover relationships among a dependent variable and one or more independent variables, and helps determine how the dependent variable's values change in relation to the independent variable values. An example is using geographic location, mean income, average summer temperature and square footage to predict the future value of a property.
• Association and item set mining, which looks for statistically relevant relationships among variables in a large data set. For example, this could help direct call-center representatives to offer specific incentives based on the caller's customer segment, duration of relationship and type of complaint.
• Similarity and correlation, which is used to inform undirected clustering algorithms. Similarity-scoring algorithms can be used to determine the similarity of entities placed in a candidate cluster.
• Neural networks, which are used in undirected analysis for machine learning based on adaptive weighting and approximation.

This is just a subset of the types of analyses used for predictive and prescriptive analytics. In addition, different vendors are likely to provide a variety of algorithms supporting each of the different methods.

The advanced analytics market

The market for advanced analytics tools has evolved over time, and the types of tools that are available vary in degree of maturity and, consequently, in capability and ease of use. For example, there are tools with relatively long histories from some mega-vendors like IBM, Oracle and SAS. Other large vendors have acquired companies whose tools have a more recent history, such as those provided by Microsoft, Dell, Teradata and SAP.

A number of smaller companies provide big data analytics products, including Angoss, Predixion, Alteryx, Alpine Data Labs, Pentaho, KNIME and RapidMiner. In some cases, companies have developed their own suite of algorithms. Others have adapted the open source statistical R language and provide predictive and prescriptive modeling capabilities using R's features, or use the software from the open source Weka project.

A third category of products are those available as open source technologies. Examples include the previously mentioned R language, the Mahout software distribution that's part of the Hadoop stack, and Weka.

In some of these cases (particularly with the mega-vendors), the big data analytics tools are incorporated into larger big data enterprise suites. In others, the tools are sold as standalone products. In the latter case, it's the customer's job to integrate with the big data platform being deployed. Most of the tools provide a visual interface to guide the analytics processes (data mining/discovery analysis, evaluation and scoring of models, integration with operational environments), and in most cases, the vendors provide guidance and services to get the customer up and running.

Who uses big data and advanced analytics tools?

While some individuals in the organization are looking to explore and devise new predictive models, others look to embed these models within their business processes, and still others will want to understand the overall impact that these tools will have on the business. In other words, organizations that are adopting big data analytics need to accommodate a variety of user types, such as:

• The data scientist, who likely performs more complex analyses involving more complex data types and is familiar with how underlying models are designed and implemented to assess inherent dependencies or biases.
• The business analyst, who is likely a more casual user looking to use the tools for proactive data discovery or visualization of existing information, as well as some predictive analytics.
• The business manager, who is looking to understand the models and conclusions.
• IT developers, who support all the prior categories of users.

All of these roles would typically work together in the model development lifecycle. The data scientist subjects a swath of big data sets to the undirected analyses provided, and looks for any patterns that would be of business interest. After engaging the business analyst to review how the models work and evaluate how each of those discovered models or patterns could potentially positively affect the business, the business manager and IT teams are brought in to embed or integrate the models into business processes or devise new processes around the models.

From a market perspective, though, it's interesting to consider the types of businesses that are embracing big data analytics. Many of the early users of big data technologies were Internet companies (e.g., Google, Yahoo, Facebook, LinkedIn and Netflix) or analytics services providers. Each of these companies relied on operational and analytical applications requiring fast-flowing streams of data to ingest, process, analyze, and then feed the results back to continuously improve performance.

As appetites for data expand among companies in more mainstream industries, big data analytics has found a place in a more general corporate population. In the past, the cost factors for a large-scale analytics platform would have limited the adoption to only the very largest businesses. However, the availability of utility-style hosted big data platforms (such as those available via Amazon Web Services) and the ability to instantiate big data platforms such as Hadoop on-premises without a large investment have reduced the barrier to entry. In addition, open data sets and accessibility to fire hose data feeds from social media channels provide the raw material for larger-scale data analyses when blended with internal data sets.

Larger businesses may still opt for high-end big data analytics tools, but lower-cost alternatives deployed on cost-effective platforms enable small and medium-size businesses to evaluate and launch big data analytics programs and achieve the desired business improvement results.

Now that we've examined the different types of tools and their uses, the next step is to determine how these tools could benefit your company. By taking a look at the various use cases for big data analytics, you will begin to see where a general big data analytics capability can be leveraged for creating and enhancing value.

SOURCE: SearchBusinessAnalytics

What is innovation process management (IPM) definition

According to the consultancy Gartner Inc., companies that can successfully manage and maintain innovation within the workplace can increase revenue, improve operational effectiveness, and pursue new business models.

Common tools or strategies used to elicit this creativity from employees include brainstorming, virtual prototyping, product lifecycle management, idea management, product line planning, portfolio management and more.

Innovation processes often fall into two categories: "pushed" or "pulled." A pushed process is when a company has access to existing or emerging technologies and tries to find a profitable application for it. A pulled process is when the company focuses on areas where the customers' needs are not met and a solution is found.

An important aspect of keeping innovation, especially IT innovation, alive within a company is cultivating and maintaining an innovative culture.

One type of innovation culture is a formulaic innovation culture. A formulaic innovation management style instills a vision throughout the workplace and continually supports that vision through operational processes that enable employees to take measured risks. New ideas are encouraged, can come from anyone within the company and, when good ideas do surface, that idea is supported through one of the company's time-tested processes. The possible drawbacks to this type of business innovation management is that companies can begin to value the system over the breakthroughs, and the culture within the organization can become complacent.

Another type of innovation culture is an entrepreneurial innovation culture. This type of innovation culture is rare and usually features, especially early on in the company's maturity, a single innovator or leader. Steve Jobs, the cofounder of Apple Inc. was an example of the single leader inspired innovation culture, as is Mark Zuckerberg, chairman and CEO of Facebook. These types of companies are usually willing to take risks that most companies would not. These types of companies strive for major disruption rather than incremental growth and they use emerging and disruptive technologies to change how a certain product or service is used. One possible drawback is that the company can rely too heavily on the innovative leader.

Gartner's recommendation to IT leaders interested in launching an innovation management program is to follow a disciplined approach. Here are five steps Gartner recommends IT leaders and their companies take to develop an innovation management program:

1. Strategize and plan: Settle on an agreement of the vision for the initiative that is also in line with business goals. Then establish the resources and budget, and integrate the vision with IT and business plans.
2. Develop governance: Establish a process for making decisions. This includes identifying and engaging stakeholders, agreeing on who is in charge and what the flow for decision making is, and also having feedback mechanisms in place.
3. Drive change management: Have systems by which people can communicate and socialize via multiple channels; get buy-in from stakeholders at all levels; and assess which open innovation initiatives and cultural shifts will help the company optimize contributions to innovation.
4. Execute: Make sure to draw from a wide range of sources to generate ideas for innovations that will transform the business, align the initiatives with business goals, and then update and drive new elements of the initiatives in response to changing business requirements.
5. Measure and improve: Once the innovative initiative is in place, monitor and measure how it has affected business outcomes. It is also important to seek feedback from stakeholders and to continue to study innovation best practices and case studies from other organizations. Also make sure to continually drive improvements through process changes and upgrades.

SOURCE: SearchCIO