June 5, 2018 - Dale Kim | Big Data Ecosystem

Democratizing Big Data: The Power of a Unified View of Data as a Competitive Tool

Industry experts and data-driven corporations around the world know there are far too few data scientists to meet the current demand, and those that are available are expensive. As a result, we’re seeing the emergence of power users referred to as “citizen data scientists” who are able to leverage powerful big data analytics tools. This trend is a key aspect of the “democratization of big data”— which is the larger movement that is enabling a wider base of users to quickly access the data they need to perform analyses and enterprise reporting of their own making, with minimal IT involvement.

But there’s a lot more to it than that if you want to move closer towards true self-service BI. For example, you need to free your data from your traditional silos. In this fourth blog post in our series that’s based on the ebook, “Modern Business Intelligence: Leading the Way to Big Data Success,” we highlight the fact that decision makers and influencers need a unified view of such data, and getting that view has traditionally involved a number of highly labor- and therefore cost-intensive processes. One potential solution that some organizations have experimented with is dumping data into a single, large “data lake” that holds massive amounts of raw big data in its native format until it’s needed.

Data Democratization in Action: MarketShare

If you want to truly “democratize” your data, you need to find it a good place to live, such as in Hadoop. You can’t expect to move it around anytime you need to process it in a different way. Then, you’ll need an intelligent layer that sits above your data that can quickly and transparently integrate all the varied types of data. Ideally, this is a visualization tool that provides a unified view of all the data, regardless of its source. We like to think of this as a top-down “smart” layer.

MarketShare, a fast-growing marketing analytics technology provider to major brands, has put the
“democratization of big data” concept to use in its own services. Acquired by Neustar a few years ago, MarketShare offers decision analytics and prescriptive data-driven recommendations to help clients optimize marketing spending.

Neustar wanted to improve their decision analytics for online and mobile campaigns that they offered to large-scale digital marketers. Specifically, Neustar wanted to provide dynamic rather than predefined reporting capabilities supplied by tools that more of their business analysts could leverage. In the past, it would take MarketShare a full day and a half to develop customer-specific data sets, transform them, and load the data sets into an Oracle database. Since analysts had to wait for data to be moved into a relational DBMS, they ended up with static, predefined reports.

To solve this problem, Neustar turned to Arcada Data. Our native visual analytics and BI platform gave their business analysts the ability to drill down into the raw data details on individual customer interactions. Our solution eliminated labor- and time-consuming data extraction and data movement, so analysts could point directly to data stored in cloud platforms for fast ad hoc visualizations. Now, analysts are able to create sophisticated reports on-the-fly by selecting client-specific parameters. Even more remarkable is the fact that reporting time/effort was slashed from two full-time equivalents for three days down to one full-time equivalent for a half-day. If you want to get the details of MarketShare’s transformation, download the full case study here.

Your Democratization To-Do List

To realize the full potential of big data as a competitive tool used by non-IT business professionals, you should focus on:

  • Deploying solutions that can easily access new sources and new kinds of data.
  • Expanding and leveraging new analytics capabilities that can provide new insights, such as those derived from predictive and prescriptive analytics. Predictive analytics deliver insights into events and behaviors that haven’t even occurred yet, giving you a chance to respond before an issue arises.
  • Expanding the pool of business analysts that can exploit and leverage big data through advanced analytics tools, such as data visualization.

Most likely your organization is filled with business analysts who are eager to quickly access the data they need to perform analyses, independent of IT. Arcadia Enterprise arms business analysts with the ability to handle bigger volumes of data with advanced capabilities to develop real-time visualizations and get to insights and collaboration faster.

In the next blog post in this series, we’ll highlight four popular approaches to business intelligence architecture incorporating big data, and we’ll outline the strengths and weaknesses of each. These include the dedicated BI server (a.k.a. “traditional BI”), SQL-on-Hadoop engines with BI tools, OLAP on big data, and native visual analytics and BI for big data. That’s all included in Chapter 5: Common Approaches to Big Data Analytics. If you haven’t read the book yet, you can download it for free here either online or as a PDF.


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