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Key challenges faced during data-driven enterprise transformation

    Artificial Intelligence can serve as a catalyst for innovation and drive significant business value at organizations. While business leaders recognize its potential, most are grappling with transforming their organizations to be data-driven even before they can bring AI into the fold. They need to be strategic about the value of data and see data as capital. How they acquire, enhance, safeguard, deploy, and monetize their data can greatly impact the financial value of their company. 

    In this article, we’ll explore the key challenges business leaders face when implementing their data strategy and transforming their enterprise into a data-driven organization. 

    Key Challenges:

    1. Outdated or No Data Strategy 

    A 2021 MIT Technology Review survey of 357 business leaders revealed that only 29% of organizations surveyed had a mature and aligned data strategy. With business and technology evolving rapidly, particularly with the Covid-19 pandemic forcing many businesses to go digital, an outdated or lack of data strategy will become an impediment. Your data strategy should support a business strategy that’s relevant to today’s world. 

    1. Lack of Business Alignment and Low Sponsorship Buy-in

    Many data or analytics programs at organizations are seen as isolated technology projects and are often disconnected from business objectives. Companies tend to develop their data strategy around use cases that are not connected to their main business priorities. Leaders need to first ensure that any data or analytics projects must be aligned to the business strategy and priorities and should support the business goals that can be delivered from the projects. 

    1. Many Silos and Fragmented Data 

    Today, data comes in many more forms from multiple sources and are stored in too many silos. Moreover, only a small portion of data collected is structured. Much of the growth of data can be attributed to unstructured data like media and text. A sound data strategy needs to account for both and should be able to collect data from many internal and external sources. In addition, organizations need to have a way to properly catalogue these silos of data to make it easily accessible for businesses to generate insights and value from them. 

    1. Poor Data Governance and Stewardship

    Almost all organizations grapple with issues of data governance challenges. With scale Issues like data visibility, quality, and security are common and complex. Considerations for how to use data ethically as well as the legal and privacy issues of data use need to be defined. With increasing regulatory scrutiny around storage and use of customer data, having improper data governance and stewardship practices can lead to significant loss of trust, which may be irreversible.  

    1. Inadequate Skills and Resources

    Simply investing in new data and analytics platforms is not sufficient. Gartner analysis indicates that the greatest impediment to the data-driven enterprise transformation is the organization’s inability to bridge the data and analytics skills gap. Data, analytics and technology skills are no longer highly centered in IT; business leaders need to clearly identify the skills gap and acquire the right talent and expert opinions to ensure the successful execution of data strategy. 

    1. Weak Data-Driven Culture

    The responsibility of transforming the organization to a data-driven one often falls on the hands of the IT leaders. There is a dire need for the right data culture to permeate throughout the enterprise – where every department is carrying the responsibility of leveraging data in order to generate business insights and outcomes.

    To overcome these key challenges, business leaders should:

    1. Define a clear data strategy, or update to ensure the strategy is not outdated
    2. Identify tangible use cases that align with the data strategy and balance short-term vs. long-term priorities
    3. Design a scalable data architecture required to support the use cases
    4. Establish robust data governance and appoint data stewards
    5. Build / source relevant skills and capacity for data transformation
    6. Ensure strong change management to iteratively implement the data-driven enterprise transformation plan

    Read part 2 (upcoming) to learn about the pillars of Data Strategy and how to Design your Enterprise Data Strategy