V-Soft's Corporate Headquarters

101 Bullitt Lane, Suite #205
Louisville, KY 40222

TOLL FREE: 844.425.8425
FAX: 502.412.5869

Denver, Colorado

6400 South Fiddlers Green Circle Suite #1150
Greenwood Village, CO 80111

TOLL FREE: 844.425.8425

Chicago, Illinois

208 N. Green Street, #302, Chicago, IL 60607

TOLL FREE: 844.425.8425

Madison, Wisconsin

2810 Crossroads Drive, Ste. 4000
Madison, WI 53718

TOLL FREE: 844.425.8425

Atlanta, Georgia

1255 Peachtree Parkway Suite #4201
Cumming, GA 30041

TOLL FREE: 844.425.8425

Cincinnati, Ohio

Spectrum Office Tower 11260
Chester Road Suite 350
Cincinnati, OH 45246

Phone: 513.771.0050

Raritan, New Jersey

216 Route 206 Suite 22 Hillsborough Raritan, NJ 08844

Phone: 513.771.0050

Toronto, Canada

1 St. Clair Ave W Suite #902, Toronto, Ontario, M4V 1K6

Phone: 416.663.0900

Hyderabad, India

Incor 9, 3rd Floor, Kavuri Hills
Madhapur, Hyderabad – 500033 India

PHONE: 040-48482789

Noida, India

H-110 - Sector 63 ,
NOIDA , Gautham Budh Nagar ,
UP – 201301

How to Execute a Data Strategy in the Enterprise

Data is the new currency of the digital economy. We can’t ignore the value and relevance of the data generated by our customers, partners, competitors, employees and community in day-to-day business decisions. Learn more about what a data strategy is and how to execute it in this post.

What is a Data Strategy?

First, let us start by explaining what data is. Data is considered facts and statistics that are compiled together for reference or analysis. Large compilations of data that are both structured and unstructured are referred to as Big Data, and they can be analyzed to reveal patterns and trends that will help improve your business' strategies. Big Data itself is important to any business, as it can help you detect the root cause of failures in a system, detect fraudulent activity, or even analyze and recalculate risk portfolios in minutes.

Despite its usefulness, data is considered perishable inventory and loses value proportional to its age. Because of its short shelf life, the quicker the data is processed, the more valuable it is. How fast we can exploit this data and gain a competitive advantage with it depends on how agile, clear, and nimble the enterprise's data strategy and execution plans are.

A company's data strategy should be defined and driven by their enterprise strategy, or by a newly-created Chief Data Officer/Chief Analytics Officer in concurrence with other C-level executives. The strategy should be reviewed annually, or sometimes earlier, based on the company's business model. The culture of data-decision behavior is to be nurtured across the enterprise from the lowest-level employee to the top decision maker.

advanced analytics

The Four Quadrants of Data Strategy

The four quadrant framework of a company's data strategy spans across the six functional areas: Marketing, Sales, Service, Operations, Finance, and Workforce. Each of these departments will be aligned to the overall enterprise strategy, creating the optimal value proposition and providing steady performance growth. The four quadrants are as follows:

    • Business Value: Define, measure, and improve business metrics aligned with enterprise strategy.
    • Data: Identify meaningful data sources across the enterprise and from partners, competitors, and community.
    • Tools, Process, and Platform: Evaluate and identify storage, compute, tools, data warehouse, common processes, practices, and analytics platforms.
    • Analytical Models: Identify, define, and refine right analytical models.

Executing a Data Strategy

Once a company has developed a data strategy, the methods of execution are as follows:

  1. Align an IT strategy and budget for shared data infrastructure, governance, data security, and privacy management by investing in shared tools and platforms such as Hadoop and an in-memory NoSQL data platform across the enterprise. The economies of scale reduce the cost of data storage, compute, and flow across discrete systems and common processes and framework can reduce the data transformation cycle time.
  2. Establish a centralized data science and analytics team. Create a separate IT-independent data science and analytics team under the strategy or operations functions. Its primary objective should be to create, tune, and share analytics models, and real-time dashboard across functional teams while regularly publishing a catalogue of advanced analytics, machine learning, and self-serving BI services.
  3. Learn and share insights and feedback via a monthly executive meeting. Each month, meet to discuss the definition of metrics, quality of the data, trends, and share feedback with IT and the data science team. Learning from common insights is shared, like the customer analytics data, and metrics are created and shared by marketing, sales, and service departments. For example, issues in a supply chain is reflecting the delay of shipment and causing a spike in service calls, and will directly impact next month or quarter sales.

About the Author

Mahindra.pngMahindra Dogiyal is V-Soft Consulting's Big Data & Analytics Project Manager and is based out of North Carolina. Mahindra travels the nation being responsible for formulating and implementing the big data growth strategy for V-Soft. Request some time with Mahindra and discuss Big Data strategy right here

When he is not working, Mahindra enjoys playing golf and helping with tiger preservation in India. Connect with Mahindra on LinkedIn here.

Topics: Technology, data, IT, Hadoop, Big Data, Cloud, IT Trends, Analytics, NoSQL

Get tech and IT industry Updates

Digital Workplace Transformation