APAC CIOOutlook

Advertise

with us

  • Technologies
      • Artificial Intelligence
      • Big Data
      • Blockchain
      • Cloud
      • Digital Transformation
      • Internet of Things
      • Low Code No Code
      • MarTech
      • Mobile Application
      • Security
      • Software Testing
      • Wireless
  • Industries
      • E-Commerce
      • Education
      • Logistics
      • Retail
      • Supply Chain
      • Travel and Hospitality
  • Platforms
      • Microsoft
      • Salesforce
      • SAP
  • Solutions
      • Business Intelligence
      • Cognitive
      • Contact Center
      • CRM
      • Cyber Security
      • Data Center
      • Gamification
      • Procurement
      • Smart City
      • Workflow
  • Home
  • CXO Insights
  • CIO Views
  • Vendors
  • News
  • Conferences
  • Whitepapers
  • Newsletter
  • Awards
Apac
  • Artificial Intelligence

    Big Data

    Blockchain

    Cloud

    Digital Transformation

    Internet of Things

    Low Code No Code

    MarTech

    Mobile Application

    Security

    Software Testing

    Wireless

  • E-Commerce

    Education

    Logistics

    Retail

    Supply Chain

    Travel and Hospitality

  • Microsoft

    Salesforce

    SAP

  • Business Intelligence

    Cognitive

    Contact Center

    CRM

    Cyber Security

    Data Center

    Gamification

    Procurement

    Smart City

    Workflow

Menu
    • Business Intelligence
    • Cyber Security
    • Hotel Management
    • Workflow
    • E-Commerce
    • MORE
    #

    Apac CIOOutlook Weekly Brief

    ×

    Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Apac CIOOutlook

    Subscribe

    loading

    THANK YOU FOR SUBSCRIBING

    • Home
    • Business Intelligence
    Editor's Pick (1 - 4 of 8)
    left
    BI & Analytics in Aquaculture

    Matthew Leary, CIO, Tassal Operations

    Need and Challenges of Business Intelligence for Small and Medium Enterprises

    Ashok Jade, CIO, Shalimar Paints

    Managing a Major System Change to Reap Organizational and Business Rewards that Extend beyond Technology

    Christopher Dowler, CIO, IAT Insurance Group

    Customer Data Driving Success

    David L. Stevens, CIO, Maricopa County

    Advantages of Cloud Computing for Data Analytics

    Colin Boyd, VP & CIO, Joy Global

    Is Deep Learning Overhyped?

    Ofir Shalev, CTO/CIO, CXA Group

    Technology Trends that will Shape BI in 2017

    Ramesh Munamarty, Group CIO, International SOS

    SNP: The Transformation Company: Modernizing Businesses

    CEO

    right

    Creating an effective big data strategy that delivers business value

    Dnyanesh Prabhu, Director, Data Management and Conversions COE Global Services, Fiserv

    Tweet
    content-image

    Dnyanesh Prabhu, Director, Data Management and Conversions COE Global Services, Fiserv

    We are living in a big data world. Every company has already implemented or is in the process of implementing a big data strategy. Inspite of the amount of effort and widely available knowledge, as per Gartner, around 60-85% of big data projects fail. There are many success stories which rely on survivorship bias, which skews the focus towards what went well. However, it is important to also learn from those whose efforts proved less than successful.

    “Tell me where I'm going to die, that is, so I don't go there”. –Charlie Munger

    Improving your chances of success with a big data strategy need not be daunting. The focus of this article is on aspects which improve your chances of success with big data initiatives.

    1. Create a data strategy that’s firmly aligned to the business strategy

    It is important not to race ahead and create your big data strategy in isolation before considering the wider business.

    Ideally, a data strategy follows a business strategy, with a sound business strategy relying on the accessibility of reliable and accurate internal data and current external data.

    An example to illustrate this point is the Nintendo Wii launch.

    In 2007, the video game industry was dominated by top two players Microsoft X-Box and Sony-PlayStation. Nintendo was a distant third. Instead of going head-to-head with the market leaders, Nintendo used external data about non-customers e.g. older non-gamers, parents who wanted their children to play active games. Nintendo used actionable insights from the data gathered about these non-customer groups and launched Nintendo Wii, with a focus on simplicity and interactivity, to convert these non-customers into customers.

    2. Seek executive sponsorship

    For a successful big data initiative, executive sponsorship is a must. In the best-case scenario, the data program will be backed by an executive and supported by the CEO. The key is to prioritize the use cases which are aligned to the business strategy and to find sponsors who are willing to be early adopters. It is important to get early success to earn confidence that the data team and business can work together and deliver successful outcomes. Your initial success will turn the early adopters into advocates for your data program.

    3. Aim to be a data-driven organization

    A successful big data strategy should enable the organization to transform into a data driven organization(DDO).A modern organization finds its data residing in multiple sources, in multiple formats and multiple structures (structured, unstructured and semi-structured data). To become a DDO, business communities with varying data literacy should have seamless access to data.
    Business units (BU) with strong data literacy may require access to raw and un-formatted data so that they can develop their own products. Whereas BUs with less data literacy will require access only to highly formatted data/reports/dashboards.

    Legacy data platforms based on relational databases are not optimized to ingest and process a variety of data.

    A modern data platform should have the following characteristics;

    • It can ingest internal and external data of various formats

    • It can process large volumes of data in a cost-effective method

    •It can support batch and real-time processing of data

    •It can support BI/analytics and advanced analytics solutions

    4. Deliver key functionalities as soon as possible

    “I don’t look to jump over 7-foot bars; I look around for 1-foot bars that I can step over.”- Warren Buffet

    Gone are the days when big programs were executed over multiple years to deliver EDW and BI solutions. With modern big data capabilities like Hadoop, it is easy to fall in a trap and focus on ingesting all the available internal and external data in a data lake. Instead, aim to deliver business value as early as possible by ingesting data only from data sources which will be required to the current project/program. Data end products should be released as early as possible using an MVP (Minimum Viable Product) approach where the focus will be on delivering key functionalities to customers as early as possible.

    The first use case in the program should be a trivial and simple use-case and should not be the most important and the most complex project. More often than not, the most important and complex projects run into time/effort over-run and if you do not have any success to show prior to that, there is a good chance that business may lose confidence, and shut down the entire program.

    By starting with a small and trivial task, you will get an opportunity to implement necessary tweaks/changes in people/process without a big impact on the overall program. The focus should be on incrementally delivering multiple small improvements. When individually considered, these small improvements may feel insignificant, but when combined together, they can deliver big value.

    5. Implement a change management program

    “Would you persuade, speak of interest and not of reason.” – Benjamin Franklin

    Creating actionable insights is only half the battle won. You need to have a necessary set up from the business side to take actions on those insights. That means changing the way the business operates and embedding analytics and insights based decision making in organization processes and structure. While it is important to centralize core capabilities like data engineering and analytics, use and consumption of data should be democratized. Having business buy -in and involving business from the inception of the project, provides necessary impetus for business to take action on the insights provided by your data program.

    You can help your business create and establish a successful big data practice by following the above five actions. Such steps can prove to be a cornerstone to transforming the business into a truly data driven organization and provide the “Right Data to the Right People in the Right Way”.
    tag

    Big Data

    Hadoop

    Weekly Brief

    loading
    Top 10 BI and Analytics Consulting/Service Companies - 2020
    ON THE DECK

    I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info

    Read Also

    Loading...
    Copyright © 2025 APAC CIOOutlook. All rights reserved. Registration on or use of this site constitutes acceptance of our Terms of Use and Privacy and Anti Spam Policy 

    Home |  CXO Insights |   Whitepapers |   Subscribe |   Conferences |   Sitemaps |   About us |   Advertise with us |   Editorial Policy |   Feedback Policy |  

    follow on linkedinfollow on twitter follow on rss
    This content is copyright protected

    However, if you would like to share the information in this article, you may use the link below:

    https://business-intelligence.apacciooutlook.com/views/creating-an-effective-big-data-strategy-that-delivers-business-value-nwid-6979.html