It's once again time for my company, OpenBI, to update its website. This'll be the third version since we opened for business as a data warehousing and business intelligence consultancy in 2006. Much has changed in both the marketplace and our business in the eight years but, as importantly, much has remained the same.

The OpenBI 2006 website reflected our startup mission and the market we experienced at the time. Our core business was building data warehouses/data marts, superimposing reporting and OLAP/dashboard solutions on top of the data infrastructures we built. Customers were primarily mid to large-size companies seeking traditional BI departmental solutions to measure/understand company performance. Predictive analytics were often discussed but seldom prioritized.

The biggest challenges we faced revolved on data integration – of course the data were in good shape – when business leaders first saw POC reports and dashboards where ugly data can't hide. While the initial contract was generally for a confined set of deliverables, customers would often as not come to an “aha” moment where the data/analytics clicked. They'd then ask for follow-on work to answer new questions of additional data sources. There generally was not a big upfront investment to divine an intelligence strategy/roadmap; planning occurred on an adhoc project-by-project basis.

The work began to change about four years ago. As we started to engage, many of our new prospects had a shorter business history than even OpenBI. Their focus was as much on data as a foundational business product as it was on performance management BI. These customers were all about data -- they were, indeed, data-driven enterprises.

The trend towards partnering with data-obsessed companies has only accelerated over the last few years. Some of the newcos have little legacy with traditional BI. Most are frugal, having built initial technical infrastructures on commodity hardware, the Cloud and open source software. They turn to OpenBI for assistance when they start to experience success and understand the need to exploit that goodwill quickly to secure funding.

For sure, data marts, reports and OLAP cubes remain central to our customers. But now they're seeking a deeper involvement with us at the enterprise level. Increasingly, we're collaborating on Data Strategy that that drives from science of business thinking to articulate both business performance and data product hypotheses. A significant deliverable is a go-forward roadmap that lays out a data-driven program of development projects.

The Data Architecture includes traditional data warehousing but goes beyond, acknowledging the ascent of analytical databases, visualization, machine learning, Hadoop and the Cloud. Indeed, in the not-too-distant future, some of our customers will likely access their data-drivings exclusively from Hadoop and the Cloud. They're much less concerned about fidelity to a model from the past than they are on optimal performance at minimal cost today. My irreverent partner not-so-facetiously opines “SQL on Hadoop is the new Teradata”.

Data Engineering is where the future of OpenBI meets the past. Architecting, designing and programming data is perhaps the single most important endeavor we undertake – just as it was eight years ago. Only now, the data building tools are more extensive than databases and ETL, including the expanding Hadoop ecosystem with Impala, Pig, Spark, et al. – in addition to the data science programming environments R and Python-Pandas-Sci-Kit. Traditional ETL platforms remain critical, though, and OpenBI likes none better than open source Pentaho Data Integration.

Data Analytics is BI all grown up – with in-memory computation, visualization and statistical learning now in competition with the reports, dashboards and OLAP cubes of eight years ago. The powerful Alteryx-Tableau-R collaboration well illustrates this evolution.

If Data Analytics is a next generation BI for performance management, Data Science supports the data-driven enterprise in building new products. OpenBI data scientists are tooled in ETL and OLAP for BI, as well as in Pig, Hive and Spark for big data, and the choice of R or Python for data munging and statistical learning.

Our thinking for the new OpenBI website is starting to crystallize. We like the part of our current tagline that speaks to the data-driven enterprise. We feel it also makes a lot of sense to position all our offerings in a modern data context. Data Strategy, Data Architecture, Data Engineering, Data Analytics and Data Science appear to cover the work we're now engaged in, paying homage to our continuing DW/BI work but also (we hope) forward looking to the changing business and architectural times.

I'd love to get reader reactions. Is our assessment of where the market is headed reasonable? Will data-obsessed marketing messages resonate today?

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