DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. Big Data
  4. Guiding Principles for Data-Driven Organizations

Guiding Principles for Data-Driven Organizations

Becoming a data-driven organization is certainly a challenge that many of us must take on, but that few have mastered yet.

Adi Gaskell user avatar by
Adi Gaskell
·
May. 25, 17 · Opinion
Like (5)
Save
Tweet
Share
7.85K Views

Join the DZone community and get the full member experience.

Join For Free

Big Data has received no shortage of hype in the past few years, but successful implementations are relatively thin on the ground. This post will aim to provide you with a few tips to help you get started and ensure that you set off in the right direction, courtesy of a couple of guides on how to use data effectively.

Getting the Most Out of Analytics

The first comes from a recent paper published by British data science company Tessella. The paper provides five key tips to help you get the most out of analytics:

Focus on Your Business Outcomes

Successful analytics programs start by identifying what the business is trying to achieve and what decisions must be taken to reach those goals. Only then do they assess what data and technology are needed to inform those decisions.

Think Long-Term, but Focus on Quick Wins

Many data projects fail because they are too big and take too long to deliver value, leading senior teams to lose interest. Data projects must have a pragmatic execution plan, with milestones designed to demonstrate early success. The first data project plans should focus on multiple, smaller projects, run with agility, to deliver the fast actionable results and rapid-fire value that will win over senior teams.

Who, When, and How 

Data success requires an understanding of who will use the data, when the information is needed, and how they engage with the insights being provided. By doing so, the resulting insights are presented in an appropriate manner for the decision maker. Project outputs need to be used by all sorts of people: it may be a data visualization for an expert in drug chemistry or oil well drilling, or it may be a mobile app that presents complex analytics of multiple health metrics as a simple text recommendation. Getting it wrong may mean missed opportunities, lost customers, and disillusioned staff.

Silos Out, Collaboration In

True business transformational data projects transcend traditional organisational boundaries. Companies need to adopt newly evolved structures, creating a culture where data scientists are in direct contact with the business functions, the IT departments, and the communities to which they are providing insights. Teams must be led by someone with a strong understanding of the both business context and technical challenges, these are the vital "translators" who can speak the language of the business and data scientist.

Take a Scientific Approach

Many analytics strategies fail because they put technology first; Invest into an analytics platform, a black box, which may rapidly identify trends in their data sets. However, these correlations may not be meaningful in a business context. To deliver the effective insights, the reasons for these correlations need to be fully understood.

Guiding Principles

It’s an approach that has more than a few parallels with the guiding principles of data analytics outlined in Monetizing Your Data by Andrew Wells and Kathy Chiang. They provide a number of guiding principles to help you go about your work with data in the right way.

  1. Quality data. Most of our efforts often go into cleaning up poor quality data, and without good-quality data, your efforts will inevitably fail.
  2. A specific goal. The more specific you can make your target, the more able you will be to find data to support that goal. Just doing Big Data is not enough; what is it you want to achieve?
  3. Holistic outlook. This doesn’t mean ignoring the big picture. Your project needs to fit into the goals of the wider organization.
  4. Have actions in mind. It’s easy to get carried away with technology and forget that the tech should support a clear and specific action.
  5. Provide options. Complex decisions are seldom cut and dried, so provide decision makers with a range of options, complete with probabilities attached.
  6. Trust your methods. It’s crucial to have faith in the quality of the data you work with, but also in the methods of working with it. Without both, the "consumer" can never really achieve great things with data.
  7. Know what it’s worth. Just as you should know what outcomes you want to achieve, you should also know how much those outcomes are worth to your organization.
  8. Measure your work. As with most things, you can’t judge the effectiveness of your work if you can’t measure it.

Becoming a data-driven organization is certainly a challenge that many of us must take on, but that few have mastered yet. Hopefully, some of these tips will help you on your journey.

Data science Big data

Published at DZone with permission of Adi Gaskell, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • Apache Kafka vs. Memphis.dev
  • How To Validate Three Common Document Types in Python
  • Distributed Stateful Edge Platforms
  • Best Practices for Writing Clean and Maintainable Code

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: