In today’s complex business scenario, the ability to manage torrents of business-critical data is a key driver of business success. Though most businesses have robust data management strategies in place, mid-market companies often lack the capabilities required for effective data management. A recent study revealed that less than one percent of unstructured data is leveraged for decision-making despite it comprising more than half of an organization’s overall data. With data proliferation on the rise, middle-market companies are in the midst of a paradigm shift with a core focus on building an effective data strategy. As such, data and analytics are turning out to be vital tools that help businesses make use of these data sets. But the question haunting millions of decision-makers across industries is – Can mid-market companies that aren’t built around data still compete on data and analytics? This is where a robust data strategy comes into play.
Most organizations leverage data across a variety of business functions. But only those who adopt a holistic approach to data management and develop an enterprise-grade data management strategy succeed in optimizing technology investments and lowering costs. Speak to our experts to discover how data analytics combined with industry insights can help you tackle your data management challenges.
Data Strategy Creation: Why is it essential for mid-market companies?
Data is no longer considered a byproduct but a valuable business asset that can enable decision making in real-time. The reason behind developing a robust data strategy is to maximize data usage and ensure all sources are aligned strategically, making it easy to access, share, and analyze business information.
Having a robust data strategy in place helps drive growth and business performance. It also helps standardize methods, practices, and processes to manage, manipulate and share data across the enterprise. While mid-market companies have several data management initiatives underway, most are designed to address specific needs. A data strategy establishes a unified road map that aligns these activities across each data management discipline, delivering measurable, positive outcomes.
A smooth transition to becoming an analytics first organization requires a plan focused on digital transformation and data management. And defining a data management strategy is the first step towards establishing such a plan. Request a FREE proposal for personalized recommendations on leveraging data for business decision making.
Factors Driving Data Strategy Creation
Though the stimulus for creating a data strategy can vary from business to business or industry to industry, there are four common drivers for businesses to make a note of:
Standardization of business processes
A robust data strategy unifies business processes and IT functions, making it easier for businesses to leverage tech-enabled methodologies that enhance internal and external operations.
Alignment of processes and goals
Leveraging data as an asset is crucial for mid-market companies to succeed and drive growth. This, apart from being an impetus for creating a data-driven business plan, helps reduce costs and enhance performance.
Identification of metrics and success criteria
An enterprise data strategy defines ‘success’ and ‘quality,’ thus emphasizing the need to evaluate and track initiatives across all levels of an enterprise.
Overcoming barriers to innovation
Legacy data management systems provide limited business value in terms of cost, performance, and data quality and might hinder innovative technology adoption. Notably, these barriers are both costly and complex to modify.
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A well-defined data strategy considers the current state of the enterprise data environments and operations and helps businesses focus on innovation with minimal disruption. As such, businesses that develop a comprehensive data strategy will be well-positioned to manage the challenges associated with digital transformation and innovation. Without a holistic strategy, organizations risk becoming technology debt leading to inefficient resource usage, delayed time to market, and poor data quality.