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Archive for the ‘Emerging BI Trends’ Category

What is Your Big Data Strategy…?

Big Data is big deal. Every vendor has a strategy and a suite of products. Navigating the maze and picking the right Big Data platform and tools takes some level of planning and looking beyond techie’s dream product suite. Compounding the issue is the open source option vs. going with a vendor version of the open source. Like every other new technology, product shakedowns will happen sooner or later. So picking a suite now is like betting on the stock market, exercising caution and being  conservative with long-term outlook will pay off.

Organizations tend to follow the safe route of sticking with the big vendor strategy but the downside is getting the funding and putting up with the procurement phase of waiting forever for the approval. The hard part is knowing the product landscape, assessing the strengths of each type of solution and prioritizing the short-term and long-term strategy.

I have seen smaller companies building their entire solution in open stack and don’t pay a penny for the software. bg_confusionObviously the risk and the rewards plays out.  Training the resources and hiring trained resources from the market place is a huge factor as well. Open source still has the same issues of version, bugs and compatibility, so having the knowledgeable team makes a big difference in managing the environment and the overall quality of the delivery.

But despite the confusion, there is good news. If you are in the process of figuring out how you want to play the Big Data game, big and small vendors alike are providing you with the sandbox or Dev environment almost free or for limited duration. Leveraging this option as part of the Big Data strategy will not only save money but also the learning curve. IBM Bluemix is an example of that. So does Cloudera, Datastax and the list is growing.

To maximize the benefit, follow the basic portfolio management strategy.

  • Take an inventory of tools already available within the organization
  • Identify the products which will play better with the existing tools
  • Figure out the business case and the types of tools needed to get a successful POC
  • Match the product selection with resource knowledge base
  • Get as much help from external sources (a lot of them can be free, if you have the time) from training to POC
  • Start small and use it to get the buy in for the larger project
  • Invest in developing the strategy with POC to uncover the benefits and to build strong business case

Combining this strategy with little bit external help to narrow down the selection and avoiding the pitfalls based on the industry experience will add tremendous value in navigating the complex selection process. Time to market can be drastically cut down especially when you make use of the DevOps platform on the cloud.

The direct benefits in leveraging the try-before-buy options are:

  • No Hardware / wait time or IT involvement for setting up the environment
  • All the tools are available and ready to test
  • Pricing and the product stack can be validated rather than finding out later that you need to buy one more product which is not in the budget
  • Time to market is drastically cut down
  • Initial POC and Business Case can be built with solid proof
  • Throwaway work can be minimized

Looking at the all the benefits, it is worth taking this approach especially if you are in the initial stages and you want proof before asking for the millions which is hard to justify.

Defining Big Data Prototypes – part 2

In part 1 of this series, we discussed some of the most common assumptions associated with Big Data Proof of Concept (POC) projects. Today, we’re going to begin exploring the next stage in Big Data POC definition – “The What.”

The ‘What’ for Big Data has gotten much more complicated in recent years; and now involves several key considerations:

  1. What business goals are involved – this is perhaps the most important part of defining any POC yet strangely is often ignored in many POC efforts.
  2. What scope is involved – for our purposes this means how much of the potential solution architecture will be evaluated. This can be highly targeted (database layer only) or can be comprehensive (an entire multi-tiered stack).
  3. What technology is involved – this one is tricky because often times people view a POC only in the context of proving a specific technology (or technologies). However, our recommended approach involves aligning technologies and business expectations up front – thus the technology isn’t necessarily the main driver. Once the goals are better understood then selecting the right mix of technologies becomes supremely important.  There are different types of Big Data databases and a growing list of BI platforms to choose from – these choices are not interchangeable – some are much better tailored for specific tasks  than others.
  4. What platform is needed – this is one of the first big technical decisions associated with both Big Data and Data Warehouse projects these days. While Big Data evolved sitting atop commodity hardware, now there are a huge number of device options and even Cloud platform opportunities.
  5. What technical goals or metrics are required – this consideration is of course what allows us to determine whether we’ve achieved success or not. Often times, organizations think they’re evaluating technical goals but don’t develop sufficiently detailed metrics in advance. And of course this needs to be tied to specific business goals as well.

 

Big Data POC Architecture views

Big Data POC Architecture views

 

Once we get through those first five items, we’re very close to having a POC Solution Architecture. But how is this Architecture represented and maintained? Typically, for this type of Agile project, there will be three visualizations:

  • A conceptual view that allows business stakeholders to understand the core business goals as well as technical choices (derived from the exploration above).
  • A logical view which provides more detail on some of the data structure/design and well as specific interoperability considerations (such as login between DB and analytics platform if both are present). This could be done using UML or freeform. As most of these solutions will not include Third Normal Form (3NF) Relational approaches, the data structure will not be presented using ERD diagram notation. We will discuss how to model Big Data in a future post.
  • There is also often a need to represent the core technical architecture – server information, network information and specific interface descriptions. This isn’t quite the same as a strict data model analogy (Conceptual Logical, Physical). Rather this latter representation is simply the last level of detail for the overall solution design (not merely the DBMS structure).

It is also not uncommon to represent one or more solution options in the conceptual or logical views – which helps stakeholders decide which approach to select.  Usually, the last view or POC technical architecture is completed after the selection is made.

There is another dimension to “The What” that we need to consider as well – the project framework. This project framework will likely include the following considerations:

  • Who will be involved – both from a technical and business perspective
  • Access to the capability – the interface (in some cases there won’t be open access to this and then it becomes a demo and / or presentation)
  • The processes involved – what this means essentially is that the POC is occurring in a larger context; one that likely mirrors existing processes that are either manual or handled in other systems

The POC project framework also includes identification of individual requirements, overall timeline as well as specific milestones. In other words, the POC ought to managed as a real project.  The project framework also serves as part of the “How” of the POC, but at first it represents the overall parameters of what will occur and when.

So, let’s step back a moment and take a closer look at some of the top level questions from the beginning. For example, how do you determine a Big Data POC scope? That will be my next topic in this series.

 

copyright 2014, Perficient Inc.

Defining Big Data Prototypes – Part 1

It seems as though every large organization these days is either conducting a Big Data Proof of Concept (POC) or considering doing one. Now, there are serious questions as to whether this is even the correct path towards adoption of Big Data technologies, but of course for some potential adopters it may very well be the best way to determine the real value associated with a Big Data solution.

This week, Bill Busch provided an excellent webinar on how organizations might go through the process of making that decision or business case.  For this exploration, we will assume for the sake of argument that we’ve gotten past the ‘should we do it’ stage and are now contemplating what to do and how to do it.

 

capabilityLifecycle

Capability Evolution tends to follow a familiar path…

 

Big Data POC Assumptions:

Everything starts with assumptions – and there are a number of good ones that could be considered universal for Big Data POCs (applicable in most places), these include the following:

  • When we say ‘Big Data’ what we really mean is multiple potential technologies and maybe even an entire technology stack. The days of Big Data just being entirely focused on Hadoop are long gone. The same premise still underlies the growing set of technologies but the diversity and complexity of options have increased almost exponentially.
  • Big Data is now much more focused on Analytics. This is a key and very practical consideration – re-hosting your data is one thing – re-envisioning it is a much more pragmatic or perhaps more tangible goal.
  • A Big Data POC is not just about the data or programming some application or even just the Analytics – it’s about a “Solution.” As such it ought to be viewed and managed the way your typical IT portfolio is managed – and it should be architected.
  • The point of any POC should not be to prove that the technology works – the fact is that a lot of other people have already done that. The point is determining precisely how that new technology will help your enterprise. This means that the POC ought to be more specific and more tailored to what the eventual solution may look like. The value of having the POC is to identify any initial misconceptions so that when the transition to the operational solution occurs it will have a higher likelihood of success. This is of course the definition of an Agile approach and avoids having to re-define from scratch after ‘proof’ that the technology works has been obtained. If done properly, the POC architecture will largely mirror what the eventual solution architecture will evolve into.
  • Last but not least, keep in mind that the Big Data solution will not (in 95% of the case now anyway) replace your existing data solution ecosystem. The POC needs to take that into account up front – doing so will likely improve the value of the solution and radically reduce the possibility of running into unforeseen integration issues downstream.

Perhaps the most important consideration before launching into your Big Data POC is determining the success criteria up front. What does this mean? Essentially, it requires you to determine the key problems that the solution is targeted to solve and coming up with metrics that can be objectively obtained from the solution. Those metrics can be focused both on technical and business considerations:

  • A Technical metric might be the ability update a very large data set based on rules within a specified timeframe (consistently).
  • A Business metric might be the number of user-defined reports or dashboard visualizations supported.
  • And of course both of these aspects (technical and business capability) would be governed as part of the solution.

Without the POC success criteria it would be very difficult to determine just what value adopting Big Data technology might add to your organization. This represents the ‘proof’ that either backs up or repudiates the initial business case ROI expectation.

In my next post, we will examine the process of choosing “What to select” for a Big Data POC…

 

copyright 2014, Perficient Inc.

Three Big Data Business Case Mistakes

Tomorrow I will be giving a webinar on creating business cases for Big Data. One of the reasons for the webinar was that there is very little information available on creating a Big Data business cases. Most of what is available boils down to a “trust me, Big Data will be of value.” Most information available on the internet basically states:

More information, loaded into a central Hadoop repository, will enable better analytics, thus making our company more profitable.  

Although logically, this statement seems true and most analytical companies have accepted the above statement, it illustrates the 3 most common mistakes we see in creating a business case for Big Data.

The first mistake, is not directly linking the business case to the corporate strategy. The corporate strategy is the overall approach the company is taking to create shareholder value.   By linking the business case to the objectives in the corporate strategy, one will be able to illustrate the strategic nature of Big Data and how the initiative will support the overall company goals. Read the rest of this post »

It’s all about the data, the data…

Credit_cardWhen Apple jumped into the payment processing with ApplePay, I thought this would be a great leg up for Apple. But who will be the winner and who will be the loser? Granted the payment switches from the credit card to ApplePay which indirectly pays for the purchase, who cares as long as we can charge on the card we want, right? Also what is the market share of Apple Pay going to be? Before we answer all those questions, let’s take a look at how we pay today for services and goods.

Cash may still be the king, that may very well be the last one to die, but what everyone is after is the middle class market which is fast adapting to credit cards and now to smart phones based services, dwindling check usage tells you so. With many ways of shopping using credit cards, store cards, pre-paid cards, Paypal, Internet (billpay,  bitcoin?), the convenience I see is carrying less or no cards at all. I seldom carry my store cards, especially when they can look it up.

Apple pay will be convenient, and may help get rid of the cards altogether, if it is accepted by majority of the merchants. Discover has to go through hurdles before it got accepted, so I don’t see myself getting rid of the cards in the near future, although cards may disappear before cash does.

Credit_trans

I read the news that many major merchants have signed up with Apple and I thought, what happens to the data? Who will be owning the granular consumer spend information? Before I could finish the blog I heard the news 2 major retailers pulled out of Apple. Ha, they realized it, the data is more valuable than the technology or convenience to customers. Imagine the data movement and explosion even if Apple shares the detailed information to each of the parties involved.

Apple is expected to have around 34 Million customers with an average of 200 transaction per customer it is going to explode. You can do the math, if this information has to be shared with 2- 5 parties. No wonder some retailers are wary of signing up. I won’t be surprised if each one of the financial institutions / retailers come up with their own App for payment mechanism.

In the end having the customer spend data is more valuable for the business operations, customer excellence etc. Having the right Information Governance to manage this Information asset is not only strategic but also a matter of survival to the enterprise.

Qlik Makes Sense … the .Next Big Thing?

In my blog post about ‘Qlik Leadership’ – back in April – I pointed out how Qlik was going to reinvent itself and the BI market once again. A few months later Qlik Sense was released. Qlik Sense Desktop is a Windows-based desktop application, and I view it as Qlik’s first installment on the .Next wave of innovation.

“Just as Qlik disrupted the business intelligence industry to pioneer the data discovery category, the company is now helping transform the category as it matures to governed, user-driven creation” – per TDWI (see full TDWI Article).

Here are some features of Qlik Sense:

  • Drag-and-drop – interface that allows users to create dynamic visualizations by clicking on sets of data
  • Architecture – alternative to OLAP, with clear goal to avoid boxing the user into a predefined view of data-sets
  • Data-source Agnostic – the system works with many kinds of data, and multiple data-sources
  • BD Interactive Visualization – end-users create interactive visualizations of data that can be shared with others
  • Data Storytelling – for interactive explanations and discussions for presentations, and break out data in detail
  • HTML5 – publishing that allows results to be examined in a conventional Web browser
  • Free – for personal and internal business use

These are just some of the new features in Qlik Sense leading to a more and more “consumerized” analytic software. Read the rest of this post »

The Chief Analytics Officer

One of the key points I make in our Executive Big Data Workshops is that effective use of Big Data analytics will require transforming both business and IT organizations.   Big Data with access to cross-functional data will transform the strategic processes within a company that guide long term and year to year investments. With the ability to apply machine learning, data mining, and advance analytics to view how different business processes interact with each other, companies now have empirical information for use in their strategic processes.

We are now seeing evidence of this transformation happening with the emergence of the  Chief Analytics Officer position.  As detailed in this InfoWorld article, Chief analytics officer: The ultimate big data job, it’s not about data but what you do with the data. And it is important enough to create a new position, the CAO. I recommend reading this article.

The Best Way to Limit the Value of Big Data

A few years back I worked for a client that was implementing cell level security on every data structure within their data warehouse. They had nearly 1,000 tables and 200,000 columns — yikes! Talking about administrative overhead. The logic was that data access should only be given on a need-to-know basis. The idea would be that users would have to request access to certain tables and columns.

Big DataNeed-to-know is a term frequently used in military and government institutions that refers to granting access to sensitive information to cleared individuals. This is a good concept, but the key here is the part about “granting access to SENSITIVE data.” The key is that the information has to be classified first, then need-to-know (for cleared individuals) is applied.

Most government documents are not sensitive. This allows the administrative resources to focus on the sensitive, classified information. The system for classifying information as Top Secret, Secret, and Confidential, has relatively stringent rules for, but also discourages the over classification of information. This is because when a document is classified, its use becomes limited.

This same phenomenon is true in the corporate world. The more a set of data is locked down, the less it will be used. Unnecessary limiting an information’s workers access to data obviously does not help the overall objectives of the organization. Big Data just magnifies this dynamic and unnecessarily restricting access to Big Data is the best way to limit its value. Unreasonably lock down Big Data, its value will be severely limited.
Read the rest of this post »

One Cluster To Rule Them All!

In the Hadoop space we have a number of terms for the Hadoop File System used for data management. Data Lake is probably the most popular. I have heard it called a Data Refinery as well as some other not so mentionable names. The one that has stuck with me has been is the Data Reservoir. Mainly because this most accurate water analogy to what actually happens in a Hadoop implementation that is used for data storage and integration.

Consider, that data is first landed in the Hadoop file system. This is the un-processed data just like water running into a reservoir from different sources. The data in this form in only fit for limited use, like analytics by trained power users. The data is then processed just like water is processed. Process water you end up with water that is consumable. Go one step further and distill it, and you have water that is suitable for medical applications. Data is the same way in a Big Data environment. Process it enough and one ends up with conformed dimensions and fact tables. Process it even more, and you have data that is suitable for basing bonuses or even publishing to government regulators. Read the rest of this post »

The Modern Data Warehouse Will Augment Hadoop

The data warehouse has been a part of the EIM vernacular for nearly 20 years. The vision of the single source of the truth and a single repository for reporting and analysis are two objectives that have resulted in a never-ending journey.   The data warehouse never has had enough data and the quality required for a single version of the truth demands significant investment that only rare business cases could support. Further, the role of the analytical database has generally been difficult to achieve. Ad-hoc analysis on large sets of complex data has generally been a significant challenge for the traditional data warehouse. Historically, to address this, companies have implemented appliances, analytical data marts, or a varying set of database features and compromises (think bit mapped indexing, a variety of hardware and software caching techniques, indexed stored data to name a few).   All with significant investment and usually adding significant overhead.   Read the rest of this post »