Do users complain about how slow the system is? Do you have to create a summary table for many new reports? Do you test on sample data or only run tests once or twice because things are slow? Do developers spend a lot of time clicking refresh waiting for reports and ETL to finish?

Investing in system performance can make your business intelligence team faster. Faster systems will help BI teams deliver on shorter deadlines, adapt to changing business requirements faster, and build more functionality with few resources.

Slow systems have a big impact on development and test productivity. Tests run longer or get skipped. Unnecessary summary tables and extracts take effort that could be used on new features and make batch run times longer. Highly skilled people twiddle their thumbs and surf the web waiting for jobs to finish.

BI systems have a performance problem when system speed impacts productivity. Here are some rules of thumb for understanding when your slow system is slowing down your team:

Batch and ETL Performance Problems:

• Developers check their email or surf the web while waiting for query results.
• Batch runs during business hours.
• Developers work in production because development or test environments are too slow.

Front end performance:

• You limit who can do as-hoc or analytic reporting because of performance concerns
• Reports take more than 5 seconds to open
• Filter/drill takes longer than 2 seconds
• No one outside the BI team and full-time analysts uses ad hoc
• Write new reports routinely require new summary tables or cubes

Getting to good performance

Every environment has its own challenges, but here are some common fixes. When the time comes to invest in system performance, we suggest splitting investments into quick wins (under $10,000 and less than two weeks), and larger efforts, which can take scale up to months and hundreds of thousands of dollars.

BI Performance Quick wins:

• Increase database server RAM. Database server RAM speeds up nearly all queries. If your cache hit rate dips below 98%, this is a likely source of improvement.
• Increase network bandwidth and add adapters. Network bandwidth between database, ETL, and reporting servers is a frequent bottleneck, and cheap to add. If your peak actual used bandwidth between servers is less than 70 MB/s, you have an opportunity to gain performance.
• Tune the 10 most resource intensive SQL statements. Tuning can be labor intensive, but the biggest wins usually occur in the top few statements. Consider only statements that run longer than 10 minutes for tuning.

Big wins:

• Move from a general purpose database to an analytic database. Columnar, cube, and appliance databases generate dramatic improvements over general purpose databases. Typical results are 10-100 times improvements, but the cost in rewritten ETL and front end is high.
• Switch from full refresh to change-only refreshes. Changing ETL to loading changes enables big performance gains, and lays the basis for more frequent refreshes and near-real-time.
• Add Flash storage. Flash storage is affordable and integrates well within SAN environments. Storage access performance gains can be in the 10x range.

System performance is not just an annoyance. High performing systems help your BI team deliver higher productivity and business value.

by Tom Victory, Principal Consultant

© DecisionPath Consulting, 2012

In our research, we found that most companies, regardless of size, felt that they were able to handle the volume (i.e. the amount of data being collected and analyzed) and velocity (i.e. the speed at which the data is being collected and requested).  Overall, 77% felt their IT infrastructure was adequate to handle the volume of data they would need in the near term, and 69% felt they had adequate infrastructure to handle the their companies’ data needs when it came to velocity.

An area where they weren’t as confident, was in the variety of data (i.e. the increasing sources of data created by new technology such as web logs, social media, RFID information, etc…); only 39% indicating that they had adequate infrastructure.

So what does this mean? Here are a few takeaways.

1. You might not be as prepared as you think for big (or even not-so-big) data

Business users are getting savvier. Although most companies’ IT groups felt confident in their ability to handle the volume and velocity of data that their business users require, they may not have factored in the growth in demand from those users. It won’t just be a matter of churning out more reports as more users become aware of the benefits of analytics (although we often see this “growing pain” for companies getting more mature with BI), it will also be a matter of providing more sophisticated analytical products.

2. Success with big data volume is tied to the success of enterprise data management

Many IT managers and executives view the volume of their data being a function of the number of transactions they’re expected to collect in their transactional systems. It’s actually quite a bit more complex; volume is also dictated by how a company is able to aggregate, consolidate and correlated that data.  In telecommunications and social networking, there’s a theory called “Metcalfe’s law” which says, essentially, that the value of a network grows exponentially as the number of nodes in that network grows linearly.  Applying this principle to big data, the connections between transactional data causes the volume of data to grow exponentially.

Managing this data would be challenging enough if there were a single version of the data, but for many companies, this isn’t the case. Data often proliferates around the organization and each department creates their own data sources with their own definitions, leading to conflicting, inconsistent and sometimes just wrong data. Part of a good “big data” strategy, then, is good enterprise data management. EDM helps you get control of your data. It takes a holistic view of how data is managed across the enterprise and across its lifecycle, from initial creation through eventual retirement.

3. There may be better problems to solve with your data than variety

The infrastructure needed to analyze these new forms of data does not integrate with traditional transactional systems well. The investment needed to create an integrated view of this variety of data (i.e. structured and unstructured) is, for the near term, cost prohibitive for all but a handful of companies.

This is especially true for those companies who are underutilizing their current data. And as we’ve seen from the previous survey questions – where we see companies relying mostly on traditional reporting or scorecards and dashboards – there is a significant amount of value to be gained from improving advanced and predictive analytics. In short, companies looking to achieve near term ROI from BI would be better served by improving their basic BI capabilities.

Summary

Companies that are able to develop and manage the technical needs for big data – including the creation of more sophisticated analytics products, and the development of enterprise data management programs – are one step closer to realizing significant value from their analytics programs. But they’re not there yet. The infrastructure is there, but the processes and skills needed to leverage that infrastructure for bottom line impact, more often than not, aren’t.  Next week, we’ll take a look at the non-technical challenges companies face with BI, and provide some insight in how to overcome them.

by Adrian Alleyne, Director Market Research
© DecisionPath Consulting, 2012

In Business Analytics, Business Users Don’t Know What They Don’t Have

This week, we’ll dig into a complementary finding from our report that suggests that business users don’t know what they don’t have. We asked our business users in Sales & Marketing, Operations and Finance to identify the tasks that were most important to their role. This included tasks such as “understanding the competitive landscape” (Sales and Marketing), “translating company strategy into operational plans” (Operations), and “planning forecasting and budgeting” (Finance).

We also asked our business users to rate how well their company’s business intelligence and analytics capabilities helped them to accomplish these tasks.  Our respondents indicated that they had adequate BI for only 87% of their most important tasks. Given that fewer than half of business users indicated that they were using the more sophisticated styles of business intelligence (e.g., advanced analytics and predictive analytics), it would seem as though business users may feel as though they have adequate BI, yet are leaving much of the potential to leverage BI to improve their most important business tasks untapped.

Who’s to Blame for Inadequate Business Analytics?

From our experience working with clients in business analytics, we see this scenario all too often. And even in the cases where they do know what they don’t have and would like to use more advanced analytics, we often hear business users say that they have basic information, but it’s hard to get and IT is non-responsive, and thus they have stopped asking for even entry-level BI, let alone advanced BI.

So while business users may blame their IT departments for lack of analytics capabilities, those same IT departments blame business users for not valuing analytics and making it a lower priority than other business projects. So who’s to blame? Both. Neither. From a practical standpoint, blame really doesn’t matter. What’s more important is the ability to work together to identify the areas of top business priorities, explore ways that business analytics can improve those areas, and then put in place the systems and tools that will allow business users to execute in those areas.

It’s important for these teams to figure out how to work together soon because in the next few years, they’ll find themselves deluged with new types of data coming at them more rapidly and at higher volumes. In other words, they need to get ready to leverage big data.

Next week, I’ll examine how companies feel about their ability to handle the expected increases in volume, velocity and variety of data over the next few years.

If you want a copy of the full report, you can access it here.

By Adrian Alleyne, Director of Market Research
© Decisionpath Consulting, 2012

The results were interesting: 87% selected the term “analytics,” 80% selected “reporting,” and only 47% of respondents chose the term “business intelligence.” Given that we generally define business intelligence as the process (and technology) of analyzing business information to support better decision making and improve business results, it’s interesting to see that so few business users view the term the same way.

Business Analytics vs. Business Intelligence?

Noted business analytics writer, Timo Elliot, examined the issue in his blog post “Business Analytics vs. Business Intelligence“in which he states “everybody has an opinion [on the difference between the two terms], but nobody knows, and you shouldn’t care.”  In one sense he’s right; the difference could be a matter of semantics.

But as I look at the question posed in our survey, two thoughts occurred to me.  First, while it’s hard to know if the selection of “analytics” is due to the phrasing of the question it’s interesting that the term analytics is starting to become synonymous with the processes usually associated with business intelligence.  Second, the term “business intelligence” ranking so low is important for business intelligence teams, as it suggests that there’s a disconnect for business executives between the technology and its application. We’ve found, in companies that are highly successful with business intelligence, that BI teams are tightly integrated with their business counterparts in the development, implementation, and measurement of BI projects, and that those projects are tightly aligned with core business processes.

Reporting only Scratches the Surface of BI

What also struck me about the response to this question was the frequency with which “reporting” was selected. True, reporting is a style of BI, but it’s not a particularly sophisticated style when compared to things like advanced analytics and predictive analytics. If respondents are looking at traditional “reporting” as being representative of the value of business intelligence, it’s no wonder so many companies have failed to realize significant ROI on the BI investment.

Getting More from Business Intelligence

This year, business intelligence is the top priorities for CIOs, according to researchers at Gartner. Given the responses we’ve seen in our survey, though, one has to wonder if it’s similarly a top priority for business users. In order for organizations to get more from their BI investments, it’s crucial that business users and BI teams work to get on the same page in how the technology is implemented, used, and measured.

Next time, I’ll examine the second significant finding from our report: business users don’t know what they don’t have.

If you want a copy of the full report, you can access it here.

By Adrian Alleyne, Director of Market Research

© Decisionpath Consulting, 2012

In Business Intelligence, the Faucet Gets All the Glory

In the press and among analysts, much of the attention on business intelligence gets paid to the faucet. In other words, how your company’s business data gets analyzed and displayed is constantly changing in new and shiny ways as new versions of the tools designed to present this information are released (much in the same way that new designs for faucets and other plumping fixtures are constantly on display).  But what about the underlying pluming that’s responsible for pushing that information to that front end?

Often the consumers of this data don’t think about (or care about) the infrastructure needed to deliver this information.  Until it breaks.  I recall the following question being posed on an open business intelligence forum recently: “Your company’s Business Intelligence solution just went down…no one can access it. What do you do?”

While numerous really intelligent folks provided well reasoned action plans to resolve the issue, it was Wayne Eckerson’s response that stuck with me “Get out a stopwatch and see how long it takes for your phone to ring. If people start calling within minutes, congratulations! You have built a mission-critical BI/DW environment that people depend on to do their work and perhaps even drive core business processes.”

I suppose it stuck with a lot of the readers, as it was voted the top response. It resonated with people, I think, because for folks who are responsible for keeping the day to day operations of the BI program going are often invisible to the rest of the company.

Note to Business Folks: Get out Some Galoshes for BI Success

Maybe the plumber analogy isn’t the exact fit for business intelligence teams, but having to slog through messy data while new requests keep piling up, I’m sure sometimes it feels as though that’s case.  While business people may not be able to roll up their sleeves to fix a BI “plumbing” issue, they should play an important role in making sure that the plumbing keeps flowing smoothly.

First, business users need to partner with their BI teams in the development of the BI program. In the same way that installing the plumbing in a new home requires one to know how the plumbing will be used, and what connects with what, so too should BI teams have input from their business users for how their BI infrastructure will ultimately be used.

Second, as business users get an appreciation for the complexities of keeping this infrastructure running smoothly, they are able to take more ownership of what gets put into the system. In other words, having your business users invested in data governance and data quality isn’t an easy task, but with a greater appreciation for the clogs to the system that can occur without that sense of ownership, one is more likely to have a BI solution that continues to provide value the organization.

Next Time, More on Business Analytics Research

In case you were wondering, it was an unusually complicated hair clog, and the home improvement store had a number of innovative and effective solutions to solve the problem (just like with business intelligence). They just weren’t as shiny and cool as the stuff in the fixtures aisle (just like with business intelligence). Next week, though, I promise to get more in-depth into our latest research findings.

Before delving into analytics, two quick caveats. First, before any New York Giants fans (or Patriots haters) send any nasty emails, this post isn’t an endorsement of any particular football team, but rather an attempt to examine the benefits of analytics through the lens of sports.  Second… well, go Pats!

Setting the Stage for Super Bowl Analytics

In the 1990s, the National Football League introduced two significant changes to the league: free agency and the salary cap. Free agency allowed players, once their contracts with one team had expired, to sign with another team.  The salary cap essentially limited the amount of money a team could spend on its players. The intention, and net effect, was to create parity among all the teams in the league.  In other words, in a hyper-competitive industry, all participants had relatively equal access to talent and resources.

Yet within this environment of parity, the New England Patriots have appeared in (six, counting this weekend’s game) and won (three) more Super Bowls than any other franchise over the same period. Many have attributed this success, all other things being equal, to the use of analytics by the Patriots. Here are a few of the ways head coach Bill Belichick and the Patriots have been able to use analytics for competitive advantage.

Understanding the Value of Resources through Analytics

In 2000, the Patriots drafted a relatively unknown quarterback named Tom Brady in the sixth round, the 199^th player selected overall. The Patriots current roster features 18 players who weren’t drafted by the NFL when they left college. In case you don’t follow football, Tom Brady has gone on to become one of the greatest players of all time (according to NFL Network), and those 18 undrafted players are now playing for football’s grand prize.  While all teams have access to a wealth of data on these players, the Patriots have the ability to look at that data in a unique way, and fit it into their overall system. In other words, they understand the processes that drive success for their organization and have been able to quantify and evaluate the available resources that will most positively impact those processes.

Applying Analytics to Structured Processes

The rules for American football are fairly well established: there are a fixed number of players on each side (11), and a finite number of ways for one to advance the ball (pass, run or kick), yet teams are constantly finding novel ways to combine how those eleven players advance the ball on each play. The Patriots have been known to take the application of analytics in play calling to new heights, allowing them to select plays based on sound information given a myriad of factors in a given situation rather than relying on “conventional wisdom.” Of course, this use of analytics doesn’t always work out, but given that the Patriots have the highest winning percentage in the NFL for the past decade, this application of analytics has seemed to work for them.

Creating Long Term Value through Analytics

The Patriots are one of the most valuable franchises in the NFL according to Forbes. And while the team ranks third for overall value behind the Dallas Cowboys and the Washington Redskins, it’s seen a higher increase in value (245%) over the past decade than either.  Winning certainly helps, but many attribute the Patriots’ increase in value to the emphasis the team places on fan satisfaction analysis. The Patriots organization uses analytics to determine and improve the “total fan experience.” They even go so far as hiring 20-25 people for each home game to make quantitative measurements of stadium food, parking, personnel and bathroom cleanliness.  Many people credit the Patriots for their “attention to detail.” I think what sets them apart, however, is their analytics of the details.

What Does it All Mean?

Many organizations, in any industry, wish they were as successful as the New England Patriots.  Are analytics the only factor that has lead to their success? Of course not. But any organization looking to gain competitive advantage through analytics can benefit from the Patriots example. Companies can use analytics to better understand their competitive landscape and evaluate available resources; they can apply analytics to defined processes for improved performance; and they certainly can analyze customer information for increased loyalty. Will the Patriots winning the Super Bowl help to validate the value of analytics? No… but it sure would be nice.

By Adrian Alleyne, Patriots Fan and Director of Market Research

© 2012, DecisionPath Consulting

Gartner followed up this report with their 2012 CIO survey. One of the interesting findings was that business intelligence/business analytics jumped back up to the number one priority for CIOs after slipping to 5th place in 2011.

There are a number of reasons why this shift occurred: virtualization projects have wrapped up, or 2011 projects were more focused on cost savings vs. growth, for example.  We thought it would be amusing to take a look at how the shift took place in the following video:

While this is a somewhat tongue in cheek look at the role analytics is playing for CIOs, and for that matter organizations in general, it does underscore a theme that the two Gartner reports keep coming back to: business analytics has the potential to play a major role in supporting and shaping business strategy.

In a new report released by Gartner last week, the most salient finding was that more than 70% of BI initiatives will consist of analytics metrics that lack synchronicity with overarching business strategy.  According to the report: “Organizations often develop and deploy hindsight-oriented reports and/or query applications focusing on metrics that users may find interesting, but they don’t represent the operational or strategic controls used to facilitate business performance.” The report’s author, Andreas Bitterer, goes on to say “The immediate future of the BI landscape is one of a disconnect between marketing hype about pressing challenges on the one hand and reality on the other.”

I’m not as quick to castigate the marketing function of major BI vendors for building the hype around BI technology; some might argue that many analysts have been equally enamored with what’s next on the horizon for BI technology rather than on the business problems BI can help solve.

The Real Disconnect is Between Business Analytics and Business Process

The disconnect Gartner talks about is something we’ve seen with our clients for years.  What’s interesting is that the report predicts that this disconnect will be widespread (over 70%) for the foreseeable future. We’ve been evangelists and advocates for making the connection between business analytics and business for bottom-line impact for close to a decade, yet the issue persists.

The real disconnect comes from the perception that analytics is primarily a technology tool from business users. Our own research shows that many business users think they understand and have adequate analytics to support their core business processes; while nearly three-quarters of business users we surveyed indicate that they use traditional reporting, only 40% report using advanced or predictive analytics. Another example of this disconnect is the fact that these business users prioritize other business initiatives over analytics.  In other words, they aren’t able to connect BI with true business value.

Making the Business Analytics to Business Process Connection

I suppose the good news for any organization looking to leverage analytics to improve business performance is that most of their peers aren’t doing it…yet. But making the connection doesn’t happen overnight.  It takes an assessment of the organization’s state of readiness to leverage business analytics.  Is there a culture around process improvement and analytically driven decision making?  Is there a partnership between IT and business (e.g. is the CIO business savvy, and are departmental executives – CFO, COO, etc… – technically savvy)? Is the IT and analytics infrastructure in place to allow the organization to leverage analytics.

If the answer is no for any of the above, then some remediation has to occur to even have a chance to move out of Gartner’s “misaligned” 70%. To then move into the ranks of the minority of companies that use analytics for competitive advantage, an organization must then look at analytics from the top down, knowing the answers to some basic business questions:

1. What competitive and external factors influence my business?
2. What business strategy do I employ to compete in this environment?
3. What business processes drive this business strategy?
4. How do I measure the success of these business processes?

With the answers to these questions in place, an organization’s analytics team (and by team, I mean a collaboration between IT and line of business leadership) can then start to look at opportunities where analytics can improve how these business processes are measured. This in turn allows one to do things like provide better root cause analysis and predictive analytics.

So…Can Organizations Get Analytics Strategy Right?

While I agree with Bitterer’s observation about where companies are right now in terms of business and analytics alignment, I’m not sure that I’d agree that the majority wouldn’t be able to get there by 2014. Organizations just need to take the time to establish the process of understanding their readiness to leverage analytics, and then determine the opportunities that business analytics can offer.

By Adrian Alleyne, Director of Market Research
© DecisionPath Consulting, 2012

As we move into 2012, and dozens of lists start to crop up predicting the future of IT in 2012, I figured it may be good to take a more pragmatic approach, focusing less on an idealistic vision of what may be possible in business intelligence, and more on what is practical, and impactful for companies’ bottom lines; in other words: a pragmatic approach to BI.

Align BI and Corporate Strategy

In their annual technology survey, McKinsey & Company found that companies are shifting their decision making to incorporate more data and analytics in almost all corporate functions.  In our recent survey, though, we found that, across corporate functions, over 50% of business executives indicated that they had inadequate BI for one or more of their key areas of responsibility. 

In order to align BI and corporate strategy, there needs to be a shared commitment to the success of both areas. BI teams need to understand, and articulate in their BI products, the strategies driving each of the functional areas.  On the flip side, functional executives need to understand their companies’ BI capabilities (and limitations) and apply a business framework for using analytics.

Focus on Business Processes

In the same McKinsey survey, non-IT executives ranked the following as the top three IT priorities, in order of importance:

• Improving effectiveness of business processes
• Improving efficiency of business processes
• Providing managers with information to support planning and decision making

This is great news for BI teams, especially those who want to align BI and corporate strategy.  Business processes are key links between corporate strategy and BI. They are the mechanisms organizations through which achieve their strategies.  Business intelligence provides insight into these processes: from reporting on the historical performance of these processes, to examining the root cause of variance in these processes, to analyzing and predicting the future performance of these processes.

Having this insight, then, helps to improve the effectiveness and the efficiency of these business processes. And this insight is what managers need to support their planning and decision making.

Take a Phased Approach to Developing BI

In a previous blog post, we talk about how companies can evolve from line of business marts to enterprise data warehouses.  One of the most crucial elements for making sure this is successful, though, is identifying and prioritizing BI opportunities. Doing so allows you to develop BI projects that provide the biggest bang for the buck for the organization first. As each project is deployed and the value is realized, the validity and overall value of the BI program increases.

Focus on Transactional Data

While there has been a great deal of attention paid to the impact of big data in 2012, from a pragmatic perspective, there’s more value to be gained in the next 12 months by leveraging the traditional sources of data associated with BI. As I mention before, there’s still plenty of work to be done with this data for most companies. 

As many define it, “big data” deals with the rapid expansion in the variety and volume of information that companies will be expected to collect and analyze, and the velocity of which that data is captured and made available for analysis.  In our survey, most functional area executives didn’t see the sources of data (i.e. the variety of data) often associated with big data is crucial to their core business functions. Additionally, most BI and IT executives who responded found that their IT infrastructure was sufficient to handle the volume and velocity of data they’re expected to encounter over the next year.

Business Intelligence in 2012?

While we’re sure 2012 will be a big year for business intelligence, I think the biggest change that will matter (i.e. the most pragmatic) will be to corporate cultures, as more and more executives and managers begin to generate a higher demand for data to support their decision-making. Who knows, maybe it’ll grow to the point where Merriam-Webster will make “analytics” its word of the year for 2012.

by Adrian Alleyne, Director of Market Research

© DecisionPath Consulting, 2011

The Cost of Line of Business Marts

While these silos may serve the needs of a line of business (LOB), they come at a cost when it comes to cross-LOB or enterprise analytic and reporting needs. A lot of manual crunching, number consolidations and reconciliations across systems is done and as a result more home grown, DIY analytics and reporting solutions take shape. For enterprise analytic and reporting needs, it means no visibility or consistency of data across the organization. Unfortunately, more often than not, there is no easy means to bubble up the information for enterprise BI capabilities from these BI silos.

Five  Steps for EDW Consolidation

There is no one-size-fits-all solution for creating an EDW from a state of LOB marts, nor is there an easy solution to bringing them together. Here’s an outline of five steps you should consider as you look to consolidate your LOB marts. Of course the process is significantly more complex, but these overarching questions will help guide the consolidation process.

1. Determine the Business Need

Needless to say there has to be a business need to bring them together and to create synergies out of the existing marts. So the foundational and the most critical work revolves around getting to know from business what their information needs are, what they are able to achieve currently with their LOB data marts and what they would like to be able to achieve to run their business better. This is where you would ask them about their home grown analytics or number crunching and consolidations they do outside the formalized marts. This helps to put the gaps in their information needs into perspective and provide insight into whether bringing information together would be an ideal state for information delivery and consumption or not.

2. Uncover the BI Opportunities

The enterprise information needs that are gathered in the previous step can be categorized into business intelligence opportunities (BIOs). Examples of BIO would be workforce management analysis, customer analysis, risk analysis, etc. These BIOs can be prioritized by the business for implementation in phases. Implementing the BIOs that would generate the biggest bang for the buck and also completed in a shorter timeframe would be ideal to showcase the success of the EDW efforts.

3. Assess Your Technical Infrastructure

An effort to gather the current state of the technical BI environment, business served, the environment, tools used and the infrastructure etc. should be conducted. The performance and efficiencies of the existing technical BI architectures and environments should be gauged for maturity and conformance with best practices.

4. Build a Technical Implementation Roadmap

A lot of considerations need to go into building this roadmap, some being determining the platform for target EDW, evaluating the tools needed and types of tools needed, evaluating a data architecture that would define the EDW and the follow-through implications from that architecture; deciding whether to virtualize the EDW etc. Sometimes an objective evaluation of these considerations could be done by utilizing a multi-attribute model.

5. Last but not Least: Manage Data Governance

The success of your EDW is only as good as the quality of data in it, so data governance is crucial. It helps build confidence not just in the numbers, but in the validity of BI projects, and your overall BI/DW program. In short, data governance serves as an over-arching monitor at each stage of the EDW process. I talk about it in more detail in my previous post: Get Proactive about Data Quality.

Remember, Getting to an EDW is a Journey

Finally, you cannot wake up one fine morning and have an EDW. It’s a journey, and with each phase there will be more and more business value realized from the EDW. BI is no longer a choice for a company, it has become a norm and the phased, patient and diligent efforts to build an EDW will definitely pay off in the short and long run.

by Balakrishna Dixit, Principal DW Consultant

© DecisionPath Consulting, 2011