Welcome!

Java IoT Authors: Yeshim Deniz, Pat Romanski, Liz McMillan, Zakia Bouachraoui, Carmen Gonzalez

Related Topics: @CloudExpo, Java IoT, @DXWorldExpo, @ThingsExpo

@CloudExpo: Blog Post

Leverage Data Source Discovery By @Attivio | @CloudExpo #BigData #DevOps #Microservices

Data source discovery is the engine that drives Big Data analytics

Leverage Data Source Discovery to Become a Data-Driven Organization

As enterprises capture more and more data of all types - structured, semi-structured, and unstructured - data discovery requirements for business intelligence (BI), Big Data, and predictive analytics initiatives grow more complex. A company's ability to become data-driven and compete on analytics depends on the speed with which it can provision their analytics applications with all relevant information. The task of finding data has traditionally resided with IT, but now organizations increasingly turn towards data source discovery tools to find the right data, in context, for business users, data scientists, and BI analysts. These tools provide self-service data access speeding time to insight.

Data Source Discovery: The Great Divide
Every analytics project goes through a data source discovery stage when a business user or analyst submits a request to IT to find out what data is available for solving a problem or answering a question. Eventually, IT returns a list that the user narrows to a select few sources from which IT can build a data mart. This cycle repeats until the analyst has the right mix of information.

According to Forrester, organizations spend 80 percent of any analytics initiative on data integration. That means only 20 percent remains for developing business insights. And it can be even less. One data manager from an investment bank noted that data discovery and integration consumes upwards of 90 percent of every analytics program his firm undertakes. Sound familiar?

Moreover, 80 percent of data integration is spent on data source discovery - identifying and profiling data sources. So 64 percent of an entire analytics project can be consumed by a process that typically only scratches the surface of potentially usable data. As much as 90 percent of information stored by organizations today remains unknown and untouched.

As more connected devices and the Internet of Things (IoT) send us ever larger volumes of data, the importance of data source discovery can't be ignored. That would mean ignoring critical insights that improve decision making - and leaving substantial revenue and cost savings on the table. The lack of good tools for data source discovery continues to narrow the process bottleneck between data managers who own the data and business users who need access to it.

Failure: The Consequences of the Data Process Bottleneck
A recent Gartner poll found 60 percent of Big Data projects fail to go beyond piloting and experimentation due to an inability to demonstrate value or because they cannot evolve into existing EIM processes. Looking at these failures more closely, Svetlana Sicular highlights several reasons for "Big Botched Data," two of which tie directly to data source discovery:

  • Asking the wrong questions and lacking the right skills. IT does not have the business domain expertise to pull the right data. It simply doesn't have the necessary context that business users possess. On the other hand, business users and analysts don't know where all the relevant data lives nor do they have the technical skills to access it should they be able to locate it.
  • Big Data silos. While Hadoop has made the storage problem easier, it hasn't solved the challenge of finding and sharing data across the enterprise. Often individual business units create their own Big Data environments that other groups can't access. And not only do these environments silo the data, they also making sharing insights more time consuming and difficult.

Success: What Does a Data-Driven Organization Look Like?
Data-driven organizations have learned - often the hard way - that while having an ambitious Big Data vision creates excitement, only a thorough focus on the basics produces long-term results. So these organizations invest in automated data source discovery tools, which:

  • Reduce the time spent on gathering data
  • Leverage the untapped potential in hidden information
  • Accelerate BI initiatives through data self-service

Data source discovery is the engine that drives Big Data analytics. It sets the stage for greater revenue, profitability, and operational efficiency.

More Stories By Stephen Baker

Stephen Baker is the Chief Executive Officer for Attivio, The Data Dexterity Company, where he previously served as COO. With over fifteen years of experience as a top executive within enterprise software, he has a proven ability to build and lead high performing organizations. Prior to Attivio, Stephen spent nearly six years as the President and Chief Revenue Officer for RAMP Holdings (formerly EveryZing) and, previously, served as the CEO of RB Search (a Reed Elsevier company). Earlier in his career, Stephen spent almost eight years at Fast Search & Transfer, where he served as General Manager.

Stephen holds an MBA from the University of Pennsylvania – The Wharton School as well as a BS in Music and Marketing from Hofstra University

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


IoT & Smart Cities Stories
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in ...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...