Although almost all companies have data strategies, most of them are not functional and not clearly understood. And it could be because the strategy wasn’t well thought out or they didn’t understand the proper use of data in the organization.
“Companies can face several challenges when implementing their data strategies. For example, without a proper vision and business case for data and analytics, companies may focus on implementing new technology without a clear strategy, i.e. that is, without assessing their needs. They may also not understand how data will be used and communicated across the organization, lack of proper inventory of data assets, and even have data residing in silos and of poor quality,” said Harshavardhan Godugula, Partner, Forensic & Integrity Services at EY.
The central idea behind a robust data strategy is to create a framework for the long term, with innovation at its core. Take a step back and think about the needs of the business 5-10 years down the line, and more importantly, plan for the known unknowns.
Sangeet Aggarwal, Product Manager at Housing.com, PropTiger.com and Makaan.com, mentioned that one of the biggest challenges businesses face in executing a data strategy is trying to marry Known, near-term business use cases with the data strategy.
“Current business needs often dictate data structures and warehousing, and very soon systems are no longer able to adapt to changing needs and therein lies the challenge,” said he declared.
A standardized and scalable approach to creating a holistic data strategy is essential. Industry experts believe that there are six fundamental pillars that can guide business leaders and act as a compass to extract the maximum value from data.
1. Build trust: The first pillar is to build trust from the start with key stakeholders and leaders. Most people don’t realize the exponential business benefits of leveraging customer insights that were previously hard to imagine. Present potential business benefits up front and gain trust, not just paper buy-in. You want your end users (business stakeholders and operations) to champion your data strategy from day one.
2. Data Expectations: Businesses can set business goals and expectations from data assets and analytics capabilities. For example, are they planning to develop new or existing revenue streams, improve customer experience or loyalty, improve productivity or reduce costs?
3. Data technology and architecture: Technology is a key enabler for realizing data strategy and achieving targeted business goals. The third pillar can create the right ecosystem – leveraging social, mobile, analytics, storage, cloud, IoT, AI and machine learning capabilities to process more amount and types of data . It can also allow more users to have self-service while maintaining data transparency and auditability.
4. Data governance: If left unchecked, data can lead to serious cybersecurity, privacy, and compliance risks. Introducing data governance practices as part of a data strategy is crucial to incorporating trust and improving the sustainability of data and analytics initiatives. Data governance defines data ownership across the organization – and sets policy and standards around data management practices to build trust.
5. Working model: The fifth pillar aims to define the right process, roles and responsibilities – to quickly take data and analytics projects from ideation to reality and then to scale.
6. Talent management: Companies can foster a data culture by focusing on the human element, supporting continuous learning journeys and a technology-enabled collaborative work environment for employees.
Nowadays, data is not just a by-product of an organization’s projects or processes, but is one of the most important assets of any organization. Therefore, a clear, well-understood and actionable data strategy acts as a playbook for your data throughout your organization, and it is important to define each stage of use as well as the terms and conditions.
Lack of a data strategy will make your organization slow, inefficient, less visionary and could lead your organization to face legal issues related to data privacy. The fundamental reason for the failure of a data strategy is that it is not well developed, understood and communicated in the organization.
“Weak data analytics strategy, weak commitment to data governance, poor execution and old school mindset are some of the reasons why most companies, despite their data, struggle to deliver reliable data to consume to fuel better business choices. Driving your analytics strategy with clear business goals, organizational support, appropriate governance, and agility is critical,” said said Srinath Sathyanarayana, Chief Technology Officer (CTO) at Fincare SFB.