Category Archives: Architects

It’s Easy to Solve Problems on a Whiteboard

data architecture and Markitecture

A Combination of Marketing and Architecture for IT Solutions

This post is about Markitecture – a combination of marketing and architecture for IT solutions. Whether it is on a whiteboard or a PowerPoint slide, markitecture is typically a one page informal illustration of a system’s structure and interactions. It shows the major components, their relationships and has a few carefully selected labels and text that describes the design philosophies embodied in it. While it is easy to create and there value in a markitecture, it doesn’t qualify as Architecture and it isn’t sufficient for solving the real underlying problems. (more…)

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Who is your Chief Simplification Officer?

business-architecture

Implementing a Business Architecture Practice

One of THE biggest challenges in companies today is complexity.  To be more specific, unnecessary complexity resulting from silo behaviors and piece-meal point solutions. Businesses today are already extremely complex with the challenges of multiple products, multiple channels, global scale, higher customer expectations, and rapid and constant change, so we certainly don’t want to make the IT solutions more complex than they need to be.  That said, I’m on the side of NO we don’t need a CSO as this blog recently surveyed its readers. We just need a business architecture practice that does what it’s supposed to. (more…)

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What is an Enterprise Architecture Maturity Model?

Enterprise IT is in a state of constant evolution. As a result, business processes and technologies become increasingly more difficult to change and more costly to keep up-to-date. The solution to this predicament is an Enterprise Architecture (EA) process that can provide a framework for an optimized IT portfolio. IT Optimization strategy should be based on a comprehensive set of architectural principles which ensure consistency and make IT more responsive, efficient, and economical.

The rationalization, standardization, and consolidation process helps organizations understand their current EA maturity level and move forward on the appropriate roadmap. As they undertake the IT Optimization journey, the IT architecture matures through several stages, leveraging IT Optimization Architecture Principles to attain each level of maturity.

EA Maturity

Multiple Levels of Enterprise Architecture Maturity Model

Level 1: The first step involves helping a company develop its architecture vision and operating model, with attention to cost, globalization, investiture, or whatever is driving the company strategically. Once that vision is in place, enterprise architects can guide the organization through an iterative process of rationalization, consolidation, and eventually shared-services and cloud computing.

Level 2: The rationalization exercise helps an organization identify what standards to move towards as they eliminate the complexities and silos they have built up over the years, along with the specific technologies that will help them get there.

Depending on the company, Rationalization could start with a technical discussion and be IT-driven; or it could start at a business level. For example, a company might have distributed operations across the globe and desire to consolidate and standardize its business processes. That could drive change in the IT portfolio. Or a company that has gone through mergers and acquisitions might have redundant business processes to rationalize.

Rationalizing involves understanding the current state of an organization’s IT portfolio and business processes, and then mapping business capabilities to IT capabilities. This is done by developing scoring criteria to analyze the current portfolio, and ultimately by deciding on the standards that will propel the organization forward. Standards are the outcome of a rationalization exercise.

Standardized technology represents the second level of EA maturity. Organizations at this level have evolved beyond isolated independent silos. They have well-defined corporate governance and procurement policies, which yields measurable cost savings through reduced software licenses and the elimination of redundant systems and skill sets.

Level 3: Consolidation entails reducing the footprint of your IT portfolio. That could involve consolidating the number of database servers, application servers and storage devices, consolidating redundant security platforms, or adopting virtualization, grid computing, and related consolidation initiatives.

Consolidation may be a by-product of another technology transformation, or it may be the driver of these transformations. But whatever motivates the change, the key is to be in alignment with the overall business strategy. Enterprise architects understand where the business is going so they can pick the appropriate consolidation strategy.

Level 4: One of the key outcomes of a rationalization and consolidation exercise is the creation of a strategic roadmap that continually keeps IT in line with where the business is going.

Having a roadmap is especially important when you move down the path to shared services and cloud computing. For a company that has a very complex IT infrastructure and application portfolio, having a strategic roadmap helps the organization to move forward incrementally, minimizing risk, and giving the IT department every opportunity to deliver value to the business.

Twitter @bigdatabeat

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Speed: The #1 IT Challenge

Agile Data Integration

Speed: The #1 IT Challenge

Speed is the top challenge facing IT today, and it’s reaching crisis proportions at many organizations.  Specifically, IT needs to deliver business value at the speed that the business requires.

The challenge does not end there; This has to be accomplished without compromising cost or quality. Many people have argued that you only get two out of three on the Speed/Cost/Quality triangle, but I believe that achieving this is the central challenge facing Enterprise Architects today.  Many people I talk to are looking at agile technologies, and in particular Agile Data Integration.

There have been a lot of articles written about the challenges, but it’s not all doom and gloom.  Here is something you can do right now to dramatically increase the speed of your project delivery while improving cost and quality at the same time: Take a fresh look you Agile Data Integration environment and specifically at Data Virtualization.  Data Virtualization offers the opportunity to simplify and speed up the data part of enterprise projects.  And this is the place where more and more projects are spending 40% and more of their time.  For more information and an industry perspective you can download the latest Forrester Wave report for Data Virtualization Q1 2015.

Here is a quick example of how you can use Data Virtualization technology for rapid prototyping to speed up business value delivery:

  • Use data virtualization technology to present a common view of your data to your business-IT project teams.
  • IT and business can collaborate in realtime to access and manage data from a wide variety of very large data sources – eliminating the long, slow cycles of passing specifications back and forth between business and IT.
  • Your teams can discover, profile, and manage data using a single virtual interface that hides the complexity of the underlying data.
  • By working with a virtualization layer, you are assured that your teams are using the right data and data that can by verified by linking it to a Business Glossary with clear terms, definitions, owners, and business context to reduce the chance of misunderstandings and errors.
  • Leading offerings in this space include data quality and data masking tools in the interface, ensuring that you improve data quality in the process.
  • Data virtualization means that your teams can be delivering in days rather than months and faster delivery means lower cost.

There has been a lot of interest in agile development, especially as it relates to data projects.  Data Virtualization is a key tool to accelerate your team in this direction.

Informatica has a leading position in the Forrester report due to the productivity of the Agile Data Integration environment but also because of the integration with the rest of the Informatica platform.  From an architect’s point of view it is critical to start standardizing on an enterprise data management platform.  Continuing data and data tool fragmentation will only slow down future project delivery.  The best way to deal with the growing complexity of both data and tools is to drive standardization within your organizations.

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Next Generation Planning for Agile Business Transformation

This is an age of technology disruption and digitization. Winners will be those organizations that can adapt quickly and drive business transformation on an ongoing basis.

When I first met John Schmidt Vice President of Global Integration Services at Informatica, he asked me to visualize Business Transformation as “A modern tool like the internet and Google Maps, with which planning a road trip from New York to San Francisco with a number of stops along the way to visit friends or see some sights takes just minutes. So you’re halfway through the trip and a friend calls to say he has suddenly been called out of town, you get on your mobile phone and within a few minutes, you have a new roadmap and a new plan.”

So, why is it that creating a roadmap for an enterprise initiative takes months or even years, and upon development of such a plan, it is nearly impossible to change even when new information or external events invalidate the plan? A single transformation is useful, but what you really want is the ability to transform our business on an ongoing basis. You need to be agile in planning of the transformation initiative itself. Is it even feasible to achieve a planning capability for complex enterprise initiatives that could approach the speed and agility of cross-country road-trip planning?

The short answer is YES; you can get much faster if you do three things:

First, throw out old notions of how planning in complex corporate environments is done, while keeping in mind that planning an enterprise transformation is fundamentally different than planning a focused departmental initiative.

Second, invest in tools equivalent to Google Maps for building the enterprise roadmap. Google Maps works because it leverages a database of information about roads, rules of the roads, related local services, and points of interest. In short, Google Map the enterprise, which is not as onerous as it sounds.

Third, develop a team of Enterprise Architects and planners with the skills and discipline to use the BOST™ Framework to maintain the underlying reference data about the business, its operations, the systems that support it, and the technologies that they are based on. This will provide the execution framework for your organization to deliver the data to fuel your business initiatives and digital strategy.

The results in a closer alignment of your business and IT organizations, there will be fewer errors due to communication issues, and because your business plans are linked directly to the underlying technical implementation, your business value will be delivered quicker.

BOSTThis is not some “pie in the sky” theory or a futuristic dream. What you need is a tool like Google Maps for Business Transformation. The tool is the BOST™ Toolkit leverages the BOST™ Framework, which through models, elements, and associated relationships built around an underlying Metamodel, interprets enterprise processes using a 4-dimensional view driven by business, operations, systems, and technology. Informatica in collaboration with certified partners built The BOST™ Framework. It provides an Architecture-led Planning approach to for business transformation.

Benefits of Architecture-led Planning

The Architecture-led Planning approach is effective when applied with governance and oversight. The following four features describe the benefits:

Enablement of Business and IT Collaboration – Uses a common reference model to facilitate cross-functional business alignment, as well as alignment between business and IT. The model gets everyone on the same page, regardless of line of business, location, or IT function. This model explicitly and dynamically starts with business strategy and links from there to the technical implementation.

Data-driven Planning – Being able to capture data in a structured repository helps with rapid planning. A data-driven plan makes it dynamic and adaptable to changing circumstances. When the plan changes, rather than updating dozens of documents, simply apply the change to the relevant components in the enterprise model repository and all business and technical model views that reference that component update automatically.

Cross-Functional Decision Making – Cross-functional decision-making is facilitated in several ways. First, by showing interdependencies between functions, business operations, and systems, the holistic view helps each department or team to understand the big-picture and its role in the overall process. Second, the future state architectural models are based on a view of how business operations will change. This provides the foundation to determine the business value of the initiative, measure your progress, and ultimately report the achievement of the goals. Quantifiable metrics help decision makers look beyond the subjective perspectives and agree on fact-based success metrics.

Reduced Execution Risk – Reduced execution risk results from having a robust and holistic plan based on a rigorous analysis of all the dependent enterprise components in the business, operations, systems and technology view. Risk is reduced with an effective governance discipline both from a program management as well as from an architectural change perspective.

Business Transformation with Informatica

Integrated Program Planning is for organizations that need large or complex Change Management assistance. Examples of candidates for Integrated Program Planning include:

Enterprise Initiatives: Large-scale mergers or acquisitions, switching from a product-centric operating model to more customer-centric operations, restructuring channel or supplier relationships, rationalizing the company’s product or service portfolio, or streamlining end-to-end processes such as order-to-cash, procure-to-pay, hire-to-retire or customer on-boarding.

Top-level Directives: Examples include board-mandated data governance, regulatory compliance initiatives that have broad organizational impacts such as data privacy or security, or risk management initiatives.

Expanding Departmental Solutions into Enterprise Solutions: Successful solutions in specific business areas can often be scaled-up to become cross-functional enterprise-wide initiatives. For example, expanding a successful customer master data initiative in marketing to an enterprise-wide Customer Information Management solution used by sales, product development, and customer service for an Omni-channel customer experience.

Twitter @bigdatabeat

The BOST™ Framework identifies and defines enterprise capabilities. These capabilities are modularized as reconfigurable and scalable business services. These enterprise capabilities are independent of organizational silos and politics, which provide strategists, architects, and planners the means to drive for high performance across the enterprise, regardless of the shifting set of strategic business drivers.The BOST™ Toolkit facilitates building and implementing new or improved capabilities, adjusting business volumes, and integrating with new partners or acquisitions through common views of these building blocks and through reusing solution components. In other words, Better, Faster, Cheaper projects.

The BOST View creates a visual understanding of the relationship between business functions, data, and systems. It helps with the identification of relevant operational capabilities and underlying support systems that need to change in order to achieve the organization’s strategic objectives. The result will be a more flexible business process with greater visibility and the ability to adjust to change without error.

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What’s Driving Core Banking Modernization?

Renew

What’s Driving Core Banking Modernization

When’s the last time you visited your local branch bank and spoke to a human being? How about talking to your banker over the phone?  Can’t remember?  Well you’re not alone and don’t worry, it’s not a bad thing. The days of operating physical branches with expensive workers to greet and service customers  are being replaced with more modern and customer friendly mobile banking applications that allow consumers to deposit checks from the phone, apply for a mortgage and sign closing documents electronically, to eliminating the need to go to an ATM and get physical cash by using mobile payment solutions like Apple Pay.  In fact, a new report titled ‘Bricks + Clicks: Building the Digital Branch,’ from Jeanne Capachin and Jim Marous takes an in-depth look at how banks and credit unions are changing their branch and customer channel strategies to meet the demand of today’s digital banking customer.

Why am I talking about this? These market trends are dominating the CEO and CIO agenda in today’s banking industry. I just returned from the 2015 IDC Asian Financial Congress event in Singapore where the digital journey for the next generation bank was a major agenda item. According the IDC Financial Insights, global banks will invest $31.5B USD in core banking modernization to enable these services, improve operational efficiency, and position these banks to better compete on technology and convenience across markets. Core banking modernization initiatives are complex, costly, and fraught with risks. Let’s take a closer look. (more…)

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Top 5 Big Data Mistakes

Top 5 Big Data mistakes

Top 5 Big Data mistakes

I won’t say I’ve seen it all; I’ve only scratched the surface in the past 15 years. Below are some of the mistakes I’ve made or fixed during this time.

MongoDB as your Big Data platform

Ask yourself, why am I picking on MongoDB? The NoSQL database most abused at this point is MongoDB, while Mongo has an aggregation framework that tastes like MapReduce and even a very poorly documented Hadoop connector, its sweet spot is as an operational database, not an analytical system.

RDBMS schema as files

You dumped each table from your RDBMS into a file and stored that on HDFS, you now plan to use Hive on it. You know that Hive is slower than RDBMS; it’ll use MapReduce even for a simple select. Next, let’s look at row sizes; you have flat files measured in single-digit kilobytes.

Hadoop does best on large sets of relatively flat data. I’m sure you can create an extract that’s more de-normalized.

Data Ponds

Instead of creating a single Data Lake, you created a series of data ponds or a data swamp. Conway’s law has struck again; your business groups have created their own mini-repositories and data analysis processes. That doesn’t sound bad at first, but with different extracts and ways of slicing and dicing the data, you end up with different views of the data, i.e., different answers for some of the same questions.

Schema-on-read doesn’t mean, “Don’t plan at all,” but it means “Don’t plan for every question you might ask.”

Missing use cases

Vendors, to escape the constraints of departmental funding, are selling the idea of the data lake. The byproduct of this is the business lost sight of real use cases. The data-lake approach can be valid, but you won’t get much out of it if you don’t have actual use cases in mind.

It isn’t hard to come up with use cases, but that is always an afterthought. The business should start thinking of the use cases when their databases can’t handle the load.

SQL

You like SQL. Query languages and techniques have changed with time. Today, think of Pig as PL/SQL on steroids with maybe a touch of acid.

To do a larger bit of analytics, you may need a bigger tool set like that may include Hive, Pig, MapReduce, R, and more.

Twitter @bigdatabeat

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The Sexiest Job of the 21st Century

Sexiest Job

The Sexiest Job of the 21st Century

I’ve spent most of my career working with new technology, most recently helping companies make sense of mountains of incoming data. This means, as I like to tell people, that I have the sexiest job in the 21st century.

Harvard Business Review put the data scientist into the national spotlight in their publication Data Scientist: The Sexiest Job of the 21st Century. Job trends data from Indeed.com confirms the rise in popularity for the position, showing that the number of job postings for data scientist positions increased by 15,000%.

In the meantime, the role of data scientist has changed dramatically. Data used to reside on the fringes of the operation. It was usually important but seldom vital – a dreary task reserved for the geekiest of the geeks. It supported every function but never seemed to lead them. Even the executives who respected it never quite absorbed it.

For every Big Data problem, the solution often rests on the shoulders of a data scientist. The role of the data scientist is similar in responsibility to the Wall Street “quants” of the 80s and 90s – now, these data experienced are tasked with the management of databases previously thought too hard to handle, and too unstructured to derive any value.

So, is it the sexiest job of the 21st Century?

Think of a data scientist more like the business analyst-plus, part mathematician, part business strategist, these statistical savants are able to apply their background in mathematics to help companies tame their data dragons. But these individuals aren’t just math geeks, per se.

A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a renaissance individual who really wants to learn and bring change to an organization.

If this sounds like you, the good news is demand for data scientists is far outstripping supply. Nonetheless, with the rising popularity of the data scientist – not to mention the companies that are hiring for these positions – you have to be at the top of your field to get the jobs.

Companies look to build teams around data scientists that ask the most questions about:

  • How the business works
  • How it collects its data
  • How it intends to use this data
  • What it hopes to achieve from these analyses

These questions were important because data scientists will often unearth information that can “reshape an entire company.” Obtaining a better understanding of the business’ underpinnings not only directs the data scientist’s research, but helps them present the findings and communicate with the less-analytical executives within the organization.

While it’s important to understand your own business, learning about the successes of other corporations will help a data scientist in their current job–and the next.

Twitter @bigdatabeat

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Posted in Architects, Big Data, Business/IT Collaboration, CIO, Data Governance, General, Governance, Risk and Compliance, Real-Time | Tagged , , | Leave a comment

Stop Trying to Manage Data Growth!(?)

Data Downpour

Data Downpour

Talking to architects about analytics at a recent event, I kept hearing the familiar theme; data scientists are spending 80% of their time on “data wrangling” leaving only 20% for delivering the business insights that will drive the company’s innovation.  It was clear to everybody that I spoke to that the situation will only worsen.  The coming growth everybody sees in data volume and complexity, will only lengthen the time to value.

Gartner recently predicted that:

“by 2015, 50% of organizations will give up on managing growth and will redirect funds to improve classification and analytics.”

50 percent

“by 2015, 50% of organizations will give up on managing growth and will redirect funds to improve classification and analytics.”

Some of the details of this study are interesting.  In the end, many organizations are coming to two conclusions:

  • It’s risky to delete data, so they keep it around as insurance.
  • All data has potential business value, so more organizations are keeping it around for potential analytical purposes.

The other mega-trend here is that more and more organizations are looking to compete on analytics – and they need data to do it, both internal data and external data.

From an architect’s perspective, here are several observations:

  • The floodgates are open and analytics is a top priority. Given that, the emphasis should be on architecting to manage the dramatic increases in both data quantity and data complexity rather than on trying to stop it.
  • The immediate architectural priority has to be on simplifying and streamlining your current enterprise data architecture. Break down those data silos and standardize your enterprise data management tools and processes as much as possible.  As discussed in other blogs, data integration is becoming the biggest bottleneck to business value delivery in your environment. Gartner has projected that “by 2018, more than half the cost of implementing new large systems will be spent on integration.”  The more standardized your enterprise data management architecture is, the more efficient it will be.
  • With each new data type, new data tool (Hive, Pig, etc.), and new data storage technology (Hadoop, NoSQL, etc.) ask first if your existing enterprise data management tools can handle the task before people go out and create a new “data silo” based on the cool, new technologies. Sometimes it will be necessary, but not always.
  • The focus needs to be on speeding value delivery for the business. And the key bottleneck is highly likely to be your enterprise data architecture.

Rather than focusing on managing data growth, the priority should be on managing it in the most standardized and efficient way possible.  It is time to think about enterprise data management as a function with standard processes, skills and tools (just like Finance, Marketing or Procurement.)

Several of our leading customers have built or are building a central “Data as a Service” platform within their organizations.  This is a single, central place where all developers and analysts can go to get trustworthy data that is managed by IT through a standard architecture and served up for use by all.

For more information, see “The Big Big Data Workbook

*Gartner Predicts 2015: Managing ‘Data Lakes’ of Unprecedented Enormity, December 2014  http://www.gartner.com/document/2934417#

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