Holistic Data Strategy Idea
I have written a great deal on data, including vendor innovation, data structures, and data analytics. When discussing the growth of data management, I frequently touch on broad trends and ideas, but it can be simple to miss their business-related relevance. I've observed during the many years I've worked in data technology, contract management, and as a Chief Strategy Officer that many investment managers still don't have a solid data strategy despite new technologies and procedures. I'm going to take a step back in this blog post and discuss the essential components of creating a comprehensive data strategy.
A strategy is "a plan of action or policy designed to achieve a primary or overarching purpose," Merriam Dictionary. Data strategy should assist businesses in succeeding. At the risk of oversimplifying, there are three principles for accomplishing this:
First, the data strategy should specify how data will aid in achieving the overall objectives and results of the organization.
Second, data strategy should coexist with business and digital transformation strategy; it cannot exist independently.
Third, data strategy needs to be supported by technological strategy.
Together, business strategy, data strategy, and technology strategy should be able to offer the capabilities needed to succeed in the digital era. The initial stage in this process is to define the company's vision for the data organization.
First, create a data strategy.
A data organization's vision statement might be something like this: "create trusted high-quality data and make it timely available to stakeholders to enable transparent data-driven business decisions, investment decisions, and operational decisions to lower risk, boost efficiency, and uncover insights."
An organization will advance toward data literacy and data democratization. An ability is data literacy; How many employees in your company are able to evaluate averages and make decisions based on data from simple statistical operations like correlations? How many managers can build a business case using real, precise, and pertinent data? A business user must possess data literacy in order to query data, merge data sets, visualize data, gain insights, and ultimately use data to tell a story or make decisions.
We can determine the necessary capabilities if a vision statement is created that encourages more users to access and understand data. For this illustration, I'd say that the organization's needs are as follows:
A data dictionary, data catalog, data lineage, data privacy attributes, active metadata (reference data and tags), visualization of data linkages between data domains, and data model are all required for the description, organization, and simplification of data.
Data must be verified for quality, and the organization must be able to view data quality metrics and guidelines as well as the opportunity to record exceptions and other data quality-related comments.
For business intelligence and analytics, data must be accessible and secure via a data marketplace with a streamlined semantic layer, data-level security measures, and anonymised data where necessary to eliminate biases.
Effective data governance can then be used to attain these capabilities and serve as a checkpoint or control mechanism to ensure that data is suitable for value creation.
Integrating Data Strategy with Business Strategy in Step Two
An organization's data maturity can be fueled by a compelling vision statement, but without a link to business strategy, this endeavor will be ineffective. Any comprehensive data program, whether with a short-term or long-term vision, should be informed by a company's entire business plan. Here are two instances of how this might function in real life.
The primary business objective is to make ESG measures available in the front office to assist portfolio managers if the business goal is support for an impending ESG reporting rule. As a result, data strategy objectives would probably comprise a certain period of time for integrating the following external ESG data provider, a data dictionary for ESG data elements in a data catalog tool, and ESG data elements accessible to the research team in a data marketplace.
Another illustration: If the business objective is to reduce operational risk occurrences by 80%, the main data goal might be to offer information that can be used to pinpoint the source of an operational error in a BI dashboard. This full data set might also be utilized to train machine learning models that can be used to anticipate an operational mistake incident with an appropriate data approach. In order to study the data and discover insights, such as which operational procedure is the most risky, the data science team could also access the same data in an internal data marketplace.
Business strategy should always aim to maximize income by producing business and investment intelligence, but it's important to know where more specific objectives are. By aligning with important business targets, as in the preceding two examples, data strategy can become more tactical and attainable.
3. Using technology to support your data strategy
Changes to governance processes are frequently required to realize an organization's data strategy, and these changes frequently demand for higher levels of technological assistance. In the past, IT teams collaborated with the company to develop data technology in order to build a data warehouse or conduct business intelligence reporting. Few IT companies altered their procedures to support seamless data governance when it was first introduced. Many still use Excel to track data dictionaries and people-centric data governance frameworks that lack context information about the sources, consumption locations, and uses of the data as well as few data quality indicators or regulations.
Data governance should incorporate a technology delivery process in order to adopt a technology-supported data strategy. For the majority of enterprises, this will probably involve the introduction of a new generation of tools, including platforms for metadata management or DataOps. Business analysts can use this functionality to provide data consumers with a context for the data and a data dictionary, and data stewards can specify data quality guidelines for individual data pieces. This integrated process of creating a data pipeline is frequently referred to as DataOps. At the same time, data architects can offer utilization of data with pertinent security to the end users.
To enable seamless data governance to connect technology and data strategy, solutions that enable DataOps should be added in addition to the existing master data tools, ETL tools, data warehouse tools, and data warehouse tools for an effective data strategy.
Several asset managers have modernization initiatives for their data and analytics infrastructure, and many of them may already have some or all of the features listed here. However, in my experience, the majority of managers are still unable to maximize the value of their data. Clear business goals, their alignment with data and technology goals, and the provision of the tools necessary to turn data governance into a facilitator rather than a barrier are the first steps in developing an efficient and comprehensive data strategy. Your organization will be able to make data-driven operational, investment, and business decisions thanks to a comprehensive and well-defined plan, which will lower risk, boost efficiency, and produce fresh insights.