Welcome!

@ThingsExpo Authors: Yeshim Deniz, Pat Romanski, Liz McMillan, Elizabeth White, Zakia Bouachraoui

Related Topics: @DXWorldExpo, @CloudExpo, @ThingsExpo

@DXWorldExpo: Blog Feed Post

Data Lake: Save Me More Money vs. Make Me More Money By @Schmarzo | @BigDataExpo #BigData

The data lake is a centralized repository for all the organization’s data of interest whether internally or externally generated

2016 will be the year of the data lake. But I expect that much of 2016 data lake efforts will be focused on activities and projects that save the company more money. That is okay from a foundation perspective, but IT and Business will both miss the bigger opportunity to leverage the data lake (and its associated analytics) to make the company more money.

This blog examines an approach that allows organizations to quickly achieve some “save me more money” cost benefits from their data lake without losing sight of the bigger “make me more money” payoff – by coupling the data lake with data science to optimize key business processes, uncover new monetization opportunities and create a more compelling and differentiated customer experience.

Let’s start by quickly reviewing the concept of a data lake.

The Data Lake
The data lake is a centralized repository for all the organization’s data of interest, whether internally or externally generated. The data lake frees the advanced analytics and data science teams from being held captive to the data volume (detailed transactional history at the individual level), variety (structured and unstructured data) and velocity (real-time/right-time) constraints of the data warehouse. The data lake provides a line of demarcation that supports the traditional business intelligence/data warehouse environment (for operational and management reporting and dashboards) while enabling the organization’s new advanced analytics and data science capabilities (see Figure 1).

Bill 1

Figure 1: The Data Lake

The viability of a data lake was enabled by many factors including:

  • The development of Hadoop as a scale-out processing environment. Hadoop was developed and perfected by internet giants such as Google, Yahoo, eBay and Facebook to store, manage and analyze petabytes of web, search and social media data.
  • The dramatic cost savings using open source software (Hadoop, MapReduce, Pig, Python, HBase, etc.) running on commodity servers that yields a 20x to 50x cost advantage over traditional, proprietary data warehousing technologies .
  • The ability to load data as-is, which means that a schema does NOT need to be created prior to loading the data. This supports the rapid ingestion and analysis of a wide variety of structured and unstructured data sources.

The characteristics of a data lake include:

  • Ingest. Capture data from wide range of traditional (operational, transactional) and new sources (structured and unstructured) as-is
  • Store. Store all your data in one environment for cross-functional business analysis
  • Analyze. Support the analytics and data science to uncover new customer, product, and operational insights
  • Surface. Empower front-line employees and managers, and drive a more profitable customer engagement leveraging customer, product and operational insights
  • Act. Integrate analytic insights into operational (Finance, Manufacturing, Marketing, Sales Force, Procurement, Logistics) and management (Business Intelligence reports and dashboards) systems

Data Lake Foundation: Save Me More Money
Most companies today have some level of experience with Hadoop. And many of these companies are embracing the data lake in order to drive costs out of the organization. Some of these “save me more money” areas include:

  • Data enrichment and data transformation for activities such as converting unstructured text fields into a structured format or creating new composite metrics such as recency, frequency and sequencing of customer activities.
  • ETL (Extract, Transform, Load) offload from the data warehouse. It is estimated that ETL jobs consume 40% to 80% of all the data warehouse cycles. Organizations can realize an immediate value by moving the ETL jobs off of the expensive data warehouse to the data lake.
  • Data Archiving, which provides a lower-cost way to archive or store data for historical, compliance or regulatory purposes
  • Data discovery and data visualization that supports the ability to rapidly explore and visualize a wide variety of structured and unstructured data sources.
  • Data warehouse replacement. A growing number of organizations are leveraging open-source technologies such as Hive, HBase, HAWQ and Impala to move their business intelligence workloads off of the traditional RDBMS-based data warehouse to the Hadoop-based data lake.

These customers are dealing with what I will call “data lake 1.0,” which is a technology stack that includes storage, compute and Hadoop. The savings from these “Save me more money” activities can be nice with a Return on Investment (ROI) typically in the 10% to 20% range. But if organizations stop there, then they are leaving the 5x to 10x ROI projects on the table. Do I have your attention now?

Data Lake Game-changer: Make Me More Money
Leading organizations are transitioning their data lakes to what I call “data lake 2.0” which includes the data lake 1.0 technology foundation (storage, compute, Hadoop) plus the capabilities necessary to build business-centric, analytics-enabled applications. These additional data lake 2.0 capabilities include data science, data visualization, data governance, data engineering and application development. Data lake 2.0 supports the rapid development of analytics-enabled applications, built upon the Analytics “Hub and Spoke” data lake architecture that I introduced in my blog “Why Do I Need A Data Lake?” (see Figure 2).

Bill blog2

Figure 2: Analytics Hub and Spoke Architecture

Data lake 2.0 and the Analytics “Hub and Spoke” architecture supports the development of a wide range of analytics-enabled applications including:

  • Customer Acquisition
  • Customer Retention
  • Predictive Maintenance
  • Marketing Effectiveness
  • Customer Lifetime Value
  • Demand Forecasting
  • Network Optimization
  • Risk Reduction
  • Load Balancing
  • “Smart” Products
  • Pricing Optimization
  • Yield Optimization
  • Theft Reduction
  • Revenue Protection

Note: Some organizations (public sector, federal, military, etc.) don’t really have a “make me more money” charter; so for these organizations, the focus should be on “make me more efficient.”

Big Data Value Iceberg
The game-changing business value enabled big data isn’t found in the technology-centric data lake 1.0, or the top of the iceberg. Like an iceberg, the bigger business opportunities are hiding just under the surface in data lake 2.0 (see figure 3).

bill blog3

Figure 3: Data Lake Value Iceberg

The “Save Me More Money” projects are the typical domain of IT, and that is what data lake 1.0 can deliver. However if your organization is interested in the 10x-20x ROI “Make Me More Money” opportunities, then your organization needs to aggressively continue down the data lake path to get to data lake 2.0.
10x-20x ROI projects…do I have your attention now?

Data Lake: Save Me More Money vs. Make Me More Money
Bill Schmarzo

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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...