It’s not news that business user self-service for access to information and analytics is hot. What might not be as obvious is the overhaul of information-related roles that is happening now as a result. What’s driving this? The hunger for data (big, fast, and otherwise) to feed insights, very popular data visualization tools, and new but rapidly spreading technology that puts sophisticated data exploration and manipulation tools in the hands of business users.
One impact is that classic tech management functions such as data modeling and data integration are moving into business-side roles. I can’t help but be reminded of Bill Murray’s apocalyptic vision from “Ghostbusters:” “Dogs and cats, living together… mass hysteria!” Is this the end of rational, orderly data management as we know it? Haven’t central tech management organizations always seen business-side tech decision-making (and purchasing, and implementation) as “rogue” behavior that needed to be governed out of existence? If organizations have trouble now keeping data for analytics at the right level of quality in data warehouses, won’t all this introduction of new data sources and data lakes and whatnot just make things worse?
Well, my answers are “no,” “yes,” and “no” in that order. The big changes that are afoot are not the end of order and even though “business empowerment” translates to “rogue IT” in some circles, data lakes/hubs and the infusion of 3rd party data have actually been delivering on their promise of faster, better business insights for the organizations doing it right.
When you hear the term fast data the first thought is probably the velocity of the data. Not unusual in the realm of big data where velocity is one of the V's everyone talked about. However, fast data encompasses more than a data characteristic, it is about how quickly you can get and use insight.
Working with Noel Yuhanna on an upcoming report on how to develop your data management roadmap, we found speed was a continuous theme to achieve. Clients consistently call out speed as what holds them back. How they interpret what speed means is the crux of the issue.
Technology management thinks about how quickly data is provisioned. The solution is a faster engine - in-memory grids like SAP HANA become the tool of choice. This is the wrong way to think about it. Simply serving up data with faster integration and a high performance platform is what we have always done - better box, better integration software, better data warehouse. Why use the same solution that in a year or two runs against the same wall?
The other side of the equation is that sending data out faster ignores what business stakeholders and analytics teams want. Speed to the business encompasses self-service data acquisition, faster deployment of data services, and faster changes. The reason, they need to act on the data and insights.
The right strategy is to create a vision that orients toward business outcomes. Today's reality is that we live in a world where it is no longer about first to market, we have to be about first to value. First to value with our customers, and first to value with our business capabilities. The speed at which insights are gained and ultimately how they are put to use is your data management strategy.
By now you have at least seen the cute little elephant logo or you may have spent serious time with the basic components of Hadoop like HDFS, MapReduce, Hive, Pig and most recently YARN. But do you have a handle on Kafka, Rhino, Sentry, Impala, Oozie, Spark, Storm, Tez… Giraph? Do you need a Zookeeper? Apache has one of those too! For example, the latest version of Hortonworks Data Platform has over 20 Apache packages and reflects the chaos of the open source ecosystem. Cloudera, MapR, Pivotal, Microsoft and IBM all have their own products and open source additions while supporting various combinations of the Apache projects.
After hearing the confusion between Spark and Hadoop one too many times, I was inspired to write a report, The Hadoop Ecosystem Overview, Q4 2104. For those that have day jobs that don’t include constantly tracking Hadoop evolution, I dove in and worked with Hadoop vendors and trusted consultants to create a framework. We divided the complex Hadoop ecosystem into a core set of tools that all work closely with data stored in Hadoop File System and extended group of components that leverage but do not require it.
In the past, enterprise architects could afford to think big picture and that meant treating Hadoop as a single package of tools. Not any more – you need to understand the details to keep up in the age of the customer. Use our framework to help, but please read the report if you can as I include a lot more there.
When it comes to data technology, are you lost in translation? What's the difference between data federation, virtualization, and data or information-as-a-service? Are columnar databases also relational? Does one use the same or different tools for BAM (Business Activity Monitoring) and for CEP (Complex Event Processing)? These questions are just the tip of the iceberg of a plethora of terms and definitions in the rich and complex world of enterprise data and information. Enterprise application developers, data, and information architects manage multiple challenges on a daily basis already, and the last thing they need to deal with are misunderstandings of the various data technology component definitions.
The tide is turning on privacy. Since the earliest days of the World Wide Web, there has been an increasing sense that the Internet would effectively kill privacy – and in the wake of the NSA PRISM program revelations, that sentiment was stronger than ever. However, by using our Forrester’s Technographics 360 methodology, which blends multiple qualitative and quantitative data sources, we found that attitudes on privacy are evolving: Consumers are beginning to shift from a state of apathy and resignation to caution and empowerment.
In our recently published report, we integrate Forrester's Consumer Technographics® survey data, ConsumerVoices Market Research Online Community qualitative insight, and social listening data to provide a holistic view of the changes in consumer perceptions and expectations of data privacy. In the past year, individuals have 1) become much more aware about the ways in which organizations collect, use, and share personal data and 2) have started to change their online behavior in response:
No self-respecting EA professional would enter into planning discussions with business or tech management execs without a solid grasp of the technologies available to the enterprise, right? But what about the data available to the enterprise? Given the shift towards data-driven decision-making and the clear advantages from advanced analytics capabilities, architecture professionals should be coming to the planning table with not only an understanding of enterprise data, but a working knowledge of the available third-party data that could have significant impact on your approach to customer engagement or your B2B partner strategy.
Data discussions can't be simply about internal information flow, master data, and business glossaries any more. Enterprise architects, business architects, and information architects working with business execs on tech-enabled strategies need to bring third-party data know-how to their brainstorming and planning discussions. As the data economy is still in its relatively early stages and, more to the point, as organizational responsibilities for sourcing, managing, and governing third-party data are still in their formative states, it behooves architects to take the lead in understanding the data economy in some detail. By doing so, architects can help their organizations find innovative approaches to data and analytics that have direct business impact by improving the customer experience, making your partner ecosystem more effective, or finding new revenue from data-driven products.
Coming back from the SAS Industry Analyst Event left me with one big question - Are we taking into account the recommendations or insights provided through analysis and see if they actually produced positive or negative results?
It's a big question for data governance that I'm not hearing discussed around the table. We often emphsize how data is supplied, but how it performs in it's consumed state is fogotten.
When leading business intelligence and analytics teams I always pushed to create reports and analysis that ultimately incented action. What you know should influence behavior and decisions, even if the influence was to say, "Don't change, keep up the good work!" This should be a fundamental function of data govenance. We need to care not only that the data is in the right form factor but also review what the data tells us/or how we interpret the data and did it make us better?
I've talked about the closed-loop from a master data management perspective - what you learn about customers will alter and enrich the customer master. The connection to data governance is pretty clear in this case. However, we shouldn't stop at raw data and master definitions. Our attention needs to include the data business users receive and if it is trusted and accurate. This goes back to the fact that how the business defines data is more than what exists in a database or application. Data is a total, a percentage, an index. This derived data is what the business expects to govern - and if derived data isn't supporting business objectives, that has to be incorporated into the data governance discussion.
When it comes to data investment, data management is still asking the wrong questions and positioning the wrong value. The mantra of - It's About the Business - is still a hard lesson to learn. It translates into what I see as the 7 Deadly Sins of Data Management. Here are the are - not in any particular order - and an example:
Hubris: "Business value? Yeah, I know. Tell me something I don't know."
Blindness: "We do align to business needs. See, we are building a customer master for a 360 degree view of the customer."
Vanity: "How can I optimize cost and efficiency to manage and develop data solutions?"
Gluttony: "If I build this cool solutions the business is gonna love it!"
Alien: "We need to develop an in-memory system to virtualize data and insight that materializes through business services with our application systems...[blah, blah, blah]"
Begger: "If only we were able to implement a business glossary, all our consistency issues are solved!"
Educator: "If only the business understood! I need to better educate them!."
Hadoop’s momentum is unstoppable as its open source roots grow wildly into enterprises. Its refreshingly unique approach to data management is transforming how companies store, process, analyze, and share big data. Forrester believes that Hadoop will become must-have infrastructure for large enterprises. If you have lots of data, there is a sweet spot for Hadoop in your organization. Here are five reasons firms should adopt Hadoop today:
Build a data lake with the Hadoop file system (HDFS). Firms leave potentially valuable data on the cutting-room floor. A core component of Hadoop is its distributed file system, which can store huge files and many files to scale linearly across three, 10, or 1,000 commodity nodes. Firms can use Hadoop data lakes to break down data silos across the enterprise and commingle data from CRM, ERP, clickstreams, system logs, mobile GPS, and just about any other structured or unstructured data that might contain previously undiscovered insights. Why limit yourself to wading in multiple kiddie pools when you can dive for treasure chests at the bottom of the data lake?
Enjoy cheap, quick processing with MapReduce. You’ve poured all of your data into the lake — now you have to process it. Hadoop MapReduce is a distributed data processing framework that brings the processing to the data in a highly parallel fashion to process and analyze data. Instead of serially reading data from files, MapReduce pushes the processing out to the individual Hadoop nodes where the data resides. The result: Large amounts of data can be processed in parallel in minutes or hours rather than in days. Now you know why Hadoop’s origins stem from monstrous data processing use cases at Google and Yahoo.