Question

What are the most interesting applications of data to organizational strategy?

Answer 1

Most organizations interact with, operate on, and leverage data across a vast array of business departments. Companies that want to make the transition into being a ‘data driven organization’ may entail coordinating operational business decisions to a systematic interpretation of information by deploying Data Analytics.

Companies know their data is a strategic asset and they want to utilize it to make smarter decisions; but the problem is – it’s complicated. Often data is scattered in silos, stuck in departmental systems that don’t talk well with one another, the quality of the data is poor, and the associated costs are high. In conjunction with responding to market pressures, most companies are going to prioritize the urgent, tactical, day-to-day needs over the long term strategic initiatives.

Elements of a Data Strategy for Organizations

Business Requirements

Data must address specific business needs in order to achieve strategic goals and generate real value. The first step of defining the business requirements is to identify a champion, all stakeholders, and SMEs in the organization. The champion of the Data Strategy is the executive leader who will rally support for the investment. Stakeholders and other SMEs will represent specific departments or functions within the company.

Next is to define the strategic goals and tie department activities to organization goals. It’s natural for goals to exist at the company and department level, but the stated goals for both levels should sync up. These objectives are most effectively gathered through an interview process that starts at the executive level and continues down to departments leaders. Through this process, we’ll discover what leaders are trying to measure, what they are trying to improve, questions they want answered, and ultimately, the KPIs to answer those questions.

By starting with the gathering and documenting the business requirements, we overcome the first roadblock to many IT or technical projects: knowledge of what the business is trying to accomplish.

Turning Data into Insights

A Data Strategy should provide recommendations for how to apply analytics to extract business-critical insights, and data visualization is key. Many companies still rely on Excel, email, or a legacy BI tool that doesn’t allow interaction with the data. Often a tedious, manual process is required, and relying on IT to create reports creates a bottleneck.

Data visualization tools should make the data look good, but more importantly, make the data easier to understand and interpret. Some factors that should be considered when choosing a data visualization tool include:

• Visualizations – You should be able to quickly spot trends and outliers and avoid introducing confusion via bad presentation.
• Story Telling – The dashboard should present the context of metrics and anticipate the user path of investigation and diagnosis.
• Democratization of Data – Who has access to what data? Encourage sharing and wide-spread adoption, and define common definitions and metrics across the organization.
• Data Granularity – Be able to provide right level of detail for the right audience. An analyst may need more detailed information than an executive, and some people may need drill-down capabilities.

People and Processes

Becoming data driven requires more than just technology. In this stage we look at the people in the organization and the processes related to creating, sharing, and governing data. A Data Strategy is likely going to introduce more data and data analysis and maybe new tools. Based on this, it makes sense to look at the skillsets of the users to understand their strengths and where they’ll need support. Do they need training? Do you need to hire more people? Organizational structure should also be assessed – should analysts be aligned to a business unit or to IT? And how IT will support the business in their analytics needs? Even topics like employee reviews and incentive plans should be evaluated. After all, these can be used levers to encourage employees to use data in the way the organization is intending

When employees are handed new tools but not shown how to think differently about their jobs, the end result is unlikely going to change.

Data Governance

Data Governance is what ultimately allows enterprise level sharing of data and the oil that lubricates the machinery of an analytics practice.

A data governance program will ensure that:

• Calculations used across the enterprise are determined based on input from across the enterprise;
• The right people have access to the right data; and
• Data lineage (where did the data originate and how was it transformed since that origination) is defined.