A data strategy is a long-term plan that defines the technology, processes, people, and rules needed to manage a company’s information assets. Today, all types of businesses collect massive amounts of raw data. However, if they want to use this information to make informed decisions, they will need a well-thought-out data management and analysis plan. A data strategy outlines an organization’s long-term vision for data collection, storage, sharing, and utilisation.
Developing a data strategy is critical for organisations to remain relevant, competitive, and innovative in the face of constant change. To meet business goals and unlock new value for your organisation, you must collect, organise, and act on data, such as the following:
Because it aligns data management with business strategy and data governance, your data strategy gives you a competitive advantage. It serves two primary functions.
There are additional advantages to having a good data strategy:
Most businesses face data management challenges such as data silos, data duplication across business units, inefficient data flow between departments, and a lack of clarity regarding data priorities. A data strategy enables businesses to address these issues by making data accessible and shareable in a secure manner. You can use data to meet business needs by unlocking its value. Better data alignment and access to the right data at the right time enable organisations to prepare for the unknown.
Organizations use data and analytics to better understand and serve their customers. Organizations can use data to create more value for customers and address unmet needs proactively, from online experiences to contact centres. Data also assists organisations in developing new business or monetization opportunities, as well as developing hyper-personalized products and services based on customer needs.
The Gartner Analytic Ascendancy Model categorises analytical maturity into four stages. To understand what happened and why, organisations typically begin with descriptive and diagnostic analytics. Analytical maturity occurs when an organisation transitions to predictive analytics, which uses data to predict what will occur. Prescriptive analytics is used by organisations in the final stage of maturity to achieve predetermined results. A data strategy lays out a detailed plan to assist your organisation in shifting from making decisions based on hindsight to foresight. It lays the groundwork for implementing advanced technology for improved business intelligence, such as artificial intelligence (AI) and machine learning (ML).
A data strategy lays out a plan for increasing data literacy and efficiency across the organisation. Diverse teams can collaborate to improve data quality and data collection accuracy. Furthermore, you can create customised training and learning paths for collaborators to progress from novices to experts in data management and usage.
An effective data strategy increases data security by putting in place safeguards to prevent unauthorised data access. When developing policies and processes, you can take into account all data governance rules and regulations. All operations can be meticulously planned to ensure that enterprise data management always maintains data privacy, security, and integrity.
Data defence is a centralised, command-and-control approach to data management. For each broad data category, the data architecture typically includes a single source of truth. There is, for example, a single primary source of revenue, customer, or sales data. The data systems collect information from various sources, clean it, and store it in this central repository. Thus, data defence reduces downside risk by identifying, standardising, and governing authoritative data sources in order to maintain the integrity of data flowing through the company’s internal systems. It prioritises activities such as the ones listed below.
The goal of data offence is to give more flexibility to centrally managed data management systems. It is understood that different business units interpret the same data in different ways. It allows controlled data transformations for reporting that can be reliably mapped back to the single source of truth, which accommodates those various interpretations.
Consider the following scenario both the finance and marketing departments generate monthly social media ad spending reports. Ad effectiveness is reported on the impact of spending on clicks and views in order to analyse ad effectiveness. Finance reports on how spending affects cash flow. The numbers in the reports differ, but both represent an accurate version of the truth.
For a company’s data strategy to succeed, it must incorporate both offence and defence, but striking the right balance can be difficult. Offensive activities are typically real-time operations that are more relevant to customer-facing business functions like marketing and sales. Legal, financial, compliance, and IT departments prioritise defensive activities. A well-balanced corporate strategy allows business leaders to deviate from the single source of truth in consistent ways in order to better meet business needs.
The data architecture of a company describes how it collects, stores, transforms, distributes, and consumes data. It also includes technical data management aspects such as the following:
Data architecture might, for example, input daily marketing and sales data into applications such as marketing dashboards, which then integrate and analyse the information to reveal relationships between ad spend and sales by region. Your data strategy provides the framework for data engineers to make architectural decisions that align with business objectives.
You can present your data strategy as a series of steps with a timeline for implementation. This data strategy roadmap includes guidelines for maintaining your organization’s current data maturity as well as action items for moving it forward.
Some common data strategy components to include in your roadmap are as follows:
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