Category Archives: Architects
A couple comments on the importance of integration platforms like Informatica in an EDW/Hadoop environment.
- Hadoop does mean you can do some quick and inexpensive exploratory analysis with little or no ETL. The issue is that it will not perform at the level you need to take it to production. As the webinar points out, applying some structure to the data with columnar files (not RDBMS) will dramatically speed up query performance.
- The other thing that makes an integration platform more important than ever is the explosion of data complexity. As Dr. Kimball put it:
“Integration is even more important these days because you are looking at all sorts of data sources coming in from all sorts of directions.”
To perform interesting analyses, you are going to have to be able to join data with different formats and different semantic meaning. And that is going to require integration tools.
- Thirdly, if you are going to put this data into production, you will want to incorporate data cleansing, metadata management, and possibly formal data governance to ensure that your data is trustworthy, auditable, and has business context. There is no point in serving up bad data quickly and inexpensively. The result will be poor business decisions and flawed analyses.
For Data Warehouse Architects
The challenge is to deliver actionable content from the exploding amount of data available. You will need to be constantly scanning for new sources of data and looking for ways to quickly and efficiently deliver that to the point of analysis.
For Enterprise Architects
The challenge with adding Big Data to Your EDW Architecture is to define and drive a coherent enterprise data architecture across your organization that standardizes people, processes, and tools to deliver clean and secure data in the most efficient way possible. It will also be important to automate as much as possible to offload routine tasks from the IT staff. The key to that automation will be the effective use of metadata across the entire environment to not only understand the data itself, but how it is used, by whom, and for what business purpose. Once you have done that, then it will become possible to build intelligence into the environment.
For more on Informatica’s vision for an Intelligent Data Platform and how this fits into your enterprise data architecture see Think “Data First” to Drive Business Value
On November 13, 2014, Informatica acquired the assets of Proact, whose Enterprise Architecture tools and delivery capability link architecture to business strategy. The BOST framework is now the Informatica Business Transformation Toolkit which received high marks in a recent research paper:
“(BOST) is a framework that provides four architectural views of the enterprise (Business, Operational, Systems, and Technology). This EA methodology plans and organizes capabilities and requirements at each view, based on evolving business and opportunities. It is one of the most finalized of the methodologies, in use by several large enterprises.”  (more…)
The white paper, “The Great Rethink: Building a Highly Responsive and Evolving Data Integration Architecture” by Claudia Imhoff and Joe McKendrick provides an interesting view of what such an architecture might look like. The paper describes how to move from ad hoc Data Integration to an Enterprise Data Architecture. The paper also describes an approach towards building architectural maturity and a next-generation enterprise data architecture that helps organizations to be more competitive.
Organizations that look to compete based on their data are searching for ways to design an architecture that:
- On-boards new data quickly
- Delivers clean and trustworthy data
- Delivers data at the speed required of the business
- Ensures that data is handled in secure way
- Is flexible enough to incorporate new data types and new technology
- Enables end user self-service
- Speeds up the speed of business value delivery for an organization
In my previous blog, Digital Strategy and Architecture, we discussed the demands that digital strategies are putting on enterprise data architecture in particular. Add to that the additional stress from business initiatives such as:
- Supporting new mobile applications
- Moving IT applications to the cloud – which significantly increases data management complexity
- Dealing with external data. One recent study estimates that a full 25% of the data being managed by the average organization is external data.
- Next-generation analytics and predictive analytics with Hadoop and No SQL
- Integrating analytics with applications
- Event-driven architectures and projects
- The list goes on…
The point here is that most people are unlikely to be funded to build an enterprise data architecture from scratch that can meet all these needs. A pragmatic approach would be to build out your future state architecture in each new strategic business initiative that is implemented. The real challenge of being an enterprise architect is ensuring that all of the new work does indeed add up to a coherent architecture as it gets implemented.
The “Great Rethink” white paper describes a practical approach to achieving an agile and responsive future state enterprise data architecture that will support your strategic business initiatives. It also describes a high level data integration architecture and the building blocks to achieving that architecture. This is highly recommended reading.
Also, you might recall that Informatica sponsored the Informatica Architect’s Challenge this year to design an enterprise-wide data architecture of the future. The contest has closed and we have a winner. See the site for details, Informatica Architect Challenge .
What is digitization?
It can take many forms. Here are a few types of digitization of business and examples:
|Products that add digital components||Sports equipment with sensors for immediate feedback|
|Products sold through digital channels||Conde Nast magazines|
|“Solutions” that are assembled and delivered in digital channels||USAA Insurance|
|Products that are entirely digital||Apple iTunes, eSurance, PayPal, Google|
|Companies monetizing their data||Healthcare clinical data|
The really interesting thing about digitization that you can see from some of the examples above is that it enables new competition to enter your space and competitors to leap industry boundaries. The concept of “barriers to entry” itself is eroding.
The Impact of Digitization on IT
Some interesting facts from MIT CISR’s research with Boards of Directors on digitization jumped out at me:
- Board members estimate that 32% of company’s revenues are under threat from digital disruption. This is a really stunning number when you think about it.
- Half of Board members believe that their board’s ability to oversee the strategic use of IT is “less than effective.”
- 26% of Boards hired consultants to evaluate major projects or the IT unit.
- 60% of Boards want to spend more time on digital issues next year.
The Impact of Digitization for Architects?
It boils down to two things:
- Architects need to deliver a digital platform to enable business agility in a time of increasing competition and disruption. This includes standardization around business processes, data, and the platform.
- Architects need to get more proactive in the strategy process for their organizations both in terms of the platforms and architecture and in terms of a general understanding of the challenges and opportunities that arise from digital disruption.
For more on enterprise data architecture, best practices and reference architectures see the eBook: Think “Data First” to Drive Business Value
I ended my previous blog wondering if awareness of Data Gravity should change our behavior. While Data Gravity adds Value to Big Data, I find that the application of the Value is under explained.
Exponential growth of data has naturally led us to want to categorize it into facts, relationships, entities, etc. This sounds very elementary. While this happens so quickly in our subconscious minds as humans, it takes significant effort to teach this to a machine.
A friend tweeted this to me last week: I paddled out today, now I look like a lobster. Since this tweet, Twitter has inundated my friend and me with promotions from Red Lobster. It is because the machine deconstructed the tweet: paddled <PROPULSION>, today <TIME>, like <PREFERENCE> and lobster <CRUSTACEANS>. While putting these together, the machine decided that the keyword was lobster. You and I both know that my friend was not talking about lobsters.
You may think that this maybe just a funny edge case. You can confuse any computer system if you try hard enough, right? Unfortunately, this isn’t an edge case. 140 characters has not just changed people’s tweets, it has changed how people talk on the web. More and more information is communicated in smaller and smaller amounts of language, and this trend is only going to continue.
When will the machine understand that “I look like a lobster” means I am sunburned?
I believe the reason that there are not hundreds of companies exploiting machine-learning techniques to generate a truly semantic web, is the lack of weighted edges in publicly available ontologies. Keep reading, it will all make sense in about 5 sentences. Lobster and Sunscreen are 7 hops away from each other in dbPedia – way too many to draw any correlation between the two. For that matter, any article in Wikipedia is connected to any other article within about 14 hops, and that’s the extreme. Completed unrelated concepts are often just a few hops from each other.
But by analyzing massive amounts of both written and spoken English text from articles, books, social media, and television, it is possible for a machine to automatically draw a correlation and create a weighted edge between the Lobsters and Sunscreen nodes that effectively short circuits the 7 hops necessary. Many organizations are dumping massive amounts of facts without weights into our repositories of total human knowledge because they are naïvely attempting to categorize everything without realizing that the repositories of human knowledge need to mimic how humans use knowledge.
For example – if you hear the name Babe Ruth, what is the first thing that pops to mind? Roman Catholics from Maryland born in the 1800s or Famous Baseball Player?
If you look in Wikipedia today, he is categorized under 28 categories in Wikipedia, each of them with the same level of attachment. 1895 births | 1948 deaths | American League All-Stars | American League batting champions | American League ERA champions | American League home run champions | American League RBI champions | American people of German descent | American Roman Catholics | Babe Ruth | Baltimore Orioles (IL) players | Baseball players from Maryland | Boston Braves players | Boston Red Sox players | Brooklyn Dodgers coaches | Burials at Gate of Heaven Cemetery | Cancer deaths in New York | Deaths from esophageal cancer | Major League Baseball first base coaches | Major League Baseball left fielders | Major League Baseball pitchers | Major League Baseball players with retired numbers | Major League Baseball right fielders | National Baseball Hall of Fame inductees | New York Yankees players | Providence Grays (minor league) players | Sportspeople from Baltimore | Maryland | Vaudeville performers.
Now imagine how confused a machine would get when the distance of unweighted edges between nodes is used as a scoring mechanism for relevancy.
If I were to design an algorithm that uses weighted edges (on a scale of 1-5, with 5 being the highest), the same search would yield a much more obvious result.
1895 births | 1948 deaths | American League All-Stars | American League batting champions | American League ERA champions | American League home run champions | American League RBI champions | American people of German descent | American Roman Catholics | Babe Ruth | Baltimore Orioles (IL) players | Baseball players from Maryland | Boston Braves players | Boston Red Sox players | Brooklyn Dodgers coaches | Burials at Gate of Heaven Cemetery | Cancer deaths in New York | Deaths from esophageal cancer | Major League Baseball first base coaches | Major League Baseball left fielders | Major League Baseball pitchers | Major League Baseball players with retired numbers | Major League Baseball right fielders | National Baseball Hall of Fame inductees | New York Yankees players | Providence Grays (minor league) players | Sportspeople from Baltimore | Maryland | Vaudeville performers .
Now the machine starts to think more like a human. The above example forces us to ask ourselves the relevancy a.k.a. Value of the response. This is where I think Data Gravity’s becomes relevant.
You can contact me on twitter @bigdatabeat with your comments.
We are way past the point where the architecture needs to be aligned with business goals and value delivery. That is necessary but no longer sufficient. We are now at the point where architecture needs to be central to the creation of an organization’s strategy process. Not to get hyperbolic, but anything less is risky for your career.
The Challenge: Digitization
I just came back from the MIT Center for Information Systems Research (CISR) research forum. One of the leading topics was digitization and how every business is becoming digitized. To those in the High Tech industry, this may be an “of course” topic, but to most other industries it is a wrenching change. Even those who are comfortable with the idea of digitization risk taking this too lightly.
The fact is that most products and services will have a digital component to them in the near future and an increasing number of products and services will be entirely digital. The fact is that digitization and the technologies that enable it are going to bring about a period of increased disruption. This will mean:
- New competitors. Examples: autonomous cars, sports equipment with embedded sensors that provide feedback, personal assistant fully capable of making decisions and taking action. Gartner is predicting that almost everything over $100 will have a sensor by the turn of the decade.
- New competitors jumping across industry boundaries. Examples: Apple iTunes and Google cars to name a few.
Why Architects Are Important
Architects are in a unique position to not only understand the technology trends driving this disruption, but they also to know how to leverage these trends to drive business value within their organizations. The very best architects are going to be those who are deeply involved in defining the organization strategy, not just figuring out how to implement it.
Evidence of Change
Many architects and CIOs currently report very little interest from upper management in IT. That is about to change, and quickly. At the MIT CISR forum I attended last week, they presented research around this area that is very telling:
- Half of Board of Directors members believe that their board’s ability to oversee the strategic use of IT is “less than effective.”
- 26% of Boards hired consultants to evaluate major projects or the IT unit.
- 60% of Boards want to spend more time on digital issues next year.
- Board members estimate that 32% of their company’s revenues are under threat from digital disruption.
That last bullet is the really interesting piece of research. 32% is a huge impact.
The Role of Data in Digitization
Digitization by its very nature is all about data. The winners in this space will be those that can manage and deliver relevant data the quickest. The question for architects is this: Do you have the architecture and agility to take advantage of the coming disruptions and opportunities? Are you actively advising your organization on how to leverage them? As we have documented in many previous blogs, many organizations are poorly positioned to manage their data as a discoverable and easily sharable asset. This will essential for:
- Delivering business initiatives and showing value faster (agility).
- Enabling business self-service so that IT is not the bottleneck in new analyses and decisions.
All of this requires new thinking around enterprise data architecture. For fresh thinking on this subject see Thinking “Data First” to Drive Business Value.
ERP systems were a true competitive advantage 20+ years ago, but not so today. ERP systems are a tool that gave people the best view into their business, but that is when there really were only ERP systems and Databases, but today that critical data resides in so many other areas. There are several reasons why ERP systems act as a data trap: technical factors, out of date management theory, and big data trends. First, let’s talk about management theory.
There are two fundamental concepts that have been driving much of the strategic planning in modern organizations in recent decades. The idea of economies of scale is deeply embedded in our thinking. The concept was first introduced by Adam Smith in the 18th century and reinforced throughout the 20th century by contemporaries such as Bruce Henderson. In 1968 Henderson wrote “”Costs characteristically decline by 20-30% in real terms each time accumulated experience doubles.“ The basic idea is that bigger is better. (more…)
Just last week, I visited a client for whom I had been consulting on-and-off for several years. On the meeting room wall, I saw their Enterprise Architecture portfolio, beautiful graphically designed and printed on a giant sheet of paper. My host proudly informed me how much she enjoyed putting that diagram together in 2009.
I jokingly reminded her of the famous notion of “art for art’s sake”; which is an appropriate phrase to describe what many architects are doing when populating frameworks. Indeed, when we refer to Enterprise Architecture, we must remember that the term ‘architecture’ is, itself, a metaphor.
In a tough economy, when competition is increasingly global and marketplaces are shifting, this ability to make tough decisions is going to be essential. Opportunities to save costs are going to be really valued, and architecture invariably helps companies save money. The ability to reuse, and thus rapidly seize the next related business opportunity, is also going to be highly valued.
The thing you have to be careful of is that if you see your markets disappearing, if your product is outdated, or your whole industry is redefining itself, as we have seen in things like media, you have to be ready to innovate. Architecture can restrict your innovative gene, by saying, “Wait, wait, wait. We want to slow down. We want to do things on our platform.” That can be very dangerous, if you are really facing disruptive technology or market changes.
Albert Camus wrote a famous essay exploring the Sisyphus myth called “The Myth of Sisyphus,” where he reinterpreted the central theme of the myth. Similarly, we need to challenge the myths of Enterprise Architecture and enterprise system/solution architecture in general – not meekly accept them.
IEEE says, “A key premise of this metaphor is that important decisions may be made early in system development in a manner similar to the early decision-making found in the development of civil architecture projects.”
Keep asking yourself, “When is what we built that’s stable actually constraining us too much? When is it preventing important innovation?” For many architects, that’s going to be tough, because you start to love the architecture, the standards, and the discipline. You love what you’ve created, but if it isn’t right for the market you’re facing, you have to be ready to let it go and go seize the next opportunity.
The central message is as follows: ‘documenting’ architecture in various layers of abstraction for the purposes of ‘completeness’ is plainly ridiculous. This is especially true when the effort to produce the artifacts takes such an amount of time as to make the whole collection obsolete on completion.
This got me thinking: What is the biggest bottleneck in the delivery of business value today? I know I look at things from a data perspective, but data is the biggest bottleneck. Consider this prediction from Gartner:
“Gartner predicts organizations will spend one-third more on app integration in 2016 than they did in 2013. What’s more, by 2018, more than half the cost of implementing new large systems will be spent on integration. “
When we talk about application integration, we’re talking about moving data, synchronizing data, cleansing, data, transforming data, testing data. The question for architects and senior management is this: Do you have the Data Foundation for Execution you need to drive the business results you require to compete? The answer, unfortunately, for most companies is; No.
All too often data management is an add-on to larger application-based projects. The result is unconnected and non-interoperable islands of data across the organization. That simply is not going to work in the coming competitive environment. Here are a couple of quick examples:
- Many companies are looking to compete on their use of analytics. That requires collecting, managing, and analyzing data from multiple internal and external sources.
- Many companies are focusing on a better customer experience to drive their business. This again requires data from many internal sources, plus social, mobile and location-based data to be effective.
When I talk to architects about the business risks of not having a shared data architecture, and common tools and practices for enterprise data management, they “get” the problem. So why aren’t they addressing it? The issue is that they find that they are only funded to do the project they are working on and are dealing with very demanding timeframe requirements. They have no funding or mandate to solve the larger enterprise data management problem, which is getting more complex and brittle with each new un-connected project or initiative that is added to the pile.
Studies such as “The Data Directive” by The Economist show that organizations that actively manage their data are more successful. But, if that is the desired future state, how do you get there?
Changing an organization to look at data as the fuel that drives strategy takes hard work and leadership. It also takes a strong enterprise data architecture vision and strategy. For fresh thinking on the subject of building a data foundation for execution, see “Think Data-First to Drive Business Value” from Informatica.
* By the way, Informatica is proud to announce that we are now a sponsor of the MIT Center for Information Systems Research.
Adrian gathered experts and built workgroups to dig into the issue and do root cause analysis. The workgroups came back with some pretty surprising results.
- Most people expected that “incorrect data” (missing, out of date, incomplete, or wrong data) would be the main problem. What they found was that this was only #5 on the list of issues.
- The #1 issue was “Too much data.” People working with the data could not find the data they needed because there was too much data available, and it was hard to figure out which was the data they needed.
- The #2 issue was that people did not know the meaning of data. And because people had different interpretations of the data, the often produced analyses with conflicting results. For example, “claims paid date” might mean the date the claim was approved, the date the check was cut or the date the check cleared. These different interpretations resulted in significantly different numbers.
- In third place was the difficulty in accessing the data. Their environment was a forest of interfaces, access methods and security policies. Some were documented and some not.
In one of the workgroups, a senior manager put the problem in a larger business context;
“Not being able to leverage the data correctly allows competitors to break ground in new areas before we do. Our data in my opinion is the ‘MOST’ important element for our organization.”
What started as a relatively straightforward data quality project became a more comprehensive enterprise data management initiative that could literally change the entire organization. By the project’s end, Adrian found himself leading the data strategy of the organization.
This kind of story is happening with increasing frequency across all industries as all businesses become more digital, the quantity and complexity of data grows, and the opportunities to offer differentiated services based on data grow. We are entering an era of data-fueled organizations where the competitive advantage will go to those who use their data ecosystem better than their competitors.
Gartner is predicting that we are entering an era of increased technology disruption. Organizations that focus on data as their competitive edge will have the advantage. It has become clear that a strong enterprise data architecture is central to the strategy of any industry-leading organization.
For more future-thinking on the subject of enterprise data management and data architecure see Think ‘Data First” to Drive Business Value