Tag Archives: NoSQL

Guidelines for Successful Tableau Analytics Rollout

I’ve written previously about development of Tableau analytics capability from single user to multiple teams across an organization. This article is intended for those who may have first installed Tableau Server to enable folks outside their own sphere to interact with their Tableau creations. For the way ahead, it presents a few guidelines for successful development and deployment that data analysts should internalize as their analytics product grows.

The theme is, from the very start, to develop dashboards as if they serve hundreds of users and access millions of data records. If you do that, then as your analytical tools grow in usefulness and popularity, you’ll avoid difficult conversions and retooling later. Continue reading

Meaningful Requirements Start Successful Data Projects

To me, development projects fail or succeed in the first few weeks. Once a project starts off in the wrong direction, momentum and expectations tend to prevent a return to the proper path. With today’s wealth of database options each addressing exciting new possibilities, the right choice for the application’s data foundation plays a large part in steering a project to success.

At this year’s Enterprise Data World conference, William Brooks showed the relations among different data modeling approaches, in effect detailing how to derive nine different model types from a detailed conceptual entity relationship model. Mr Brooks’ presentation hinted at a way to correctly frame up your data direction early on in a project, setting the stage for success.

According to his presentation, called “Symmetry in Modeling Approaches“, the different model types — relational, graph, dimensional, JSON, XML, and so on — all represent different perspectives on the same data relationships. Each suits a different application, like dimensional for reporting applications, data vault for data warehouses, graph databases for multi-layered search, and so on. However, if properly constructed they all map back in predictable and specific ways to a normalized entity-relationship model.

I and others write that ER modeling should be integral to requirements definition, but Mr. Brooks’ presentation implies that ER modeling can also serve as the basis for application architecture as well. Continue reading

Data Quality, Evolved

Data quality doesn’t have to be a train wreck. Increased regulatory scrutiny, NoSQL performance gains, and the needs of data scientists are quietly changing views and approaches toward data quality. The result: a pathway to optimism and data quality improvement.

Here’s how you can get on the new and improved data quality train in each of those three areas: Continue reading

No More Enterprise Data Sinks – An Agile Data Warehousing Manifesto

SinkOver the past year I’ve reviewed what seem like countless plans for enterprise data warehouses. The plans address real problems in the organizations involved: the organization needs better data to recognize trends and react faster to opportunities and challenges; business measures and analyses are unavailable because data in source systems is inconsistent, incomplete, erroneous, or contains current values but no history; and so on.

The plans detail source system data and its integration into a central data hub. But the ones I’m referring to don’t tell how the data will be delivered, or portray a specific vision of how the data is to drive business value. Instead, their business case rests on what I’ll call the “railroad hypothesis”. No one could have predicted how the railroads enabled development of the West, so the improved data infrastructure will create order of magnitude improvements in ability to access, share, and utilize data, from which order of magnitude business benefits will follow.* All too often these plans just build bridges to nowhere. Continue reading

A Short List of Accessible Big Data Training Options

As you’ve read on this site and many others, the database world is well into a transition from a relational focus to a focus on non-relational tools. While the relational approach underpins most organizations’ data management cycles, I’d venture to say that all have a big chunk of big data, NoSQL, unstructured data, and more in their five-year plans, and that chunk is what’s getting most of the executive “mind share”, to use the vernacular.

Some are well along the way in their big data learning adventure, but others haven’t started yet. One thing about this IT revolution is that there’s no shortage of highly accessible training options. But several people have complained to me about the sheer quantity of options, not to mention the sheer number of new words the novice needs to learn in order to figure out what the heck big data is.

So here’s a very short list of training options accessible to the IT professional who is a rank big data beginner, starting with a very brief classification of the tools that I hope provides a some context. Continue reading

What is Big Data Creativity and How Do You Get It?

Thomas EdisonIn a recent Smart Data Collective post, Bernard Marr cites creativity as a top big data skill, but what is creativity?

His point is, since big data applications are often off the beaten IT path, big data professionals must solve “problems that companies don’t even know they have – as their insights highlight bottlenecks or inefficiencies in the production, marketing or delivery processes,” often with “data which does not fit comfortably into tables and charts, such as human speech and writing.” Continue reading

Three things about “Interview with a Data Scientist”

Chemistry-labRecently, I posted “Interview with a Data Scientist” at my company’s blog site. In it, my friend and colleague Yan Li answers four questions about being a data scientist and what it takes to become one. In my view Yan’s responses provide a bracing reminder that data science is something truly new, but that it rests on universal principles of application development. Continue reading

To SQL or to NoSQL?

DiscDrivesRecently there was a great post at Dzone recounting how one “tech savvy startup” moved away from its NoSQL database management system to a relational one. The writer, Matt Butcher, plays out the reasons under these main points:

  1. Our data is relational
  2. We need better querying
  3. We have access to better resources

Summing up: “The bottom line: choose the right tool.” Continue reading

Relational DB Pros: The Times They Are A-Changin’

Recently I read a thoughtful post DBQuestion
at the PASS Business Analytics Conference site discussing how different the world is now for database professionals. Author Chris Webb focuses on the data science side in this post. His analysis made me think of the challenges and opportunities “big data” serves up to relational database designers.

To me these challenges are fundamental. Big Data and NoSQL bring lots of what we know about data elements, inherent data design, and data management into question. I think considering these elements closely leads to a sensible to-do list for relational database professionals. Continue reading

Skills of the Data Architect

One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key player in harnessing and capitalizing on new data technologies. Continue reading