How do businesses decide where and when to open a new store? How does a courier service track packages in real-time? How do inventory managers decide on quantities for reorders? The common answer to all of these questions “ data. Data analytics has advanced so far today that many companies have teams dedicated to just this.
According to a study, Analytics grew from $122 billion in 2015 to over $187 billion in 2020. That said, the most complicated aspect isn’t analyzing data but collecting data, cleaning it and getting the required skill support. This is why it is important to develop a sound data strategy.
What Is A Data Strategy?
Simply put, data strategy refers to a plan for preserving and improving data security, data quality and access to data across the enterprise. A comprehensive data strategy would combine all technical, organizational and compliance measures to make data more trustworthy and, in turn, make data analytics actionable. It includes all the processes, policies, architectures and standards used in data management. A good data strategy should include:
· A vision for data management
· A strong business reason/case
· Guiding principles and values for the management
· Well-defined goals for data assets
· Quantified metrics for success
· Short term and long term objectives
· Well defined roles and responsibilities
Understanding The Need For Data Strategy
Developing a data strategy can effectively solve the three main challenges that businesses face when dealing with data.
· Poor Data Quality
Data comes from many places. It’s sourced from weblogs, app activity, social media, sensors, IoT and more. That said, simply collecting data from varied sources is not enough. Before it can be used, data collectors must ensure that the data is correct, up to date, complete and unique. Without ensuring good data quality, even the best predictive and prescriptive analytics tools cannot give reliable outputs.
· Skills Shortage
Secondly, there is an acute shortage of people with the skills needed to use complex data analytical tools built into BI platforms. Thus, these tools cannot be used to their full potential. The adoption rate for self-service platforms is still quite low. Many business users continue to work with data scientists on self-service platforms.
· Absence of an Analytic Culture
In many companies, the activities involved in data analytics are not fully aligned with decision making at the corporate levels. Analytic results are often compartmentalized and not shared across the organization. This again limits the benefits the organization may experience.
How Can Data Strategy Strengthen Analytics?
An effective data strategy helps businesses maintain continuous operations while staying flexible to changes. Some of the ways it can strengthen analytics to achieve this are:
· Minimize quality risks
Quality is one of the main concerns for data analytics. Decisions taken on poor data can be expensive for the enterprise. Designing data quality enhancement processes within a data strategy ensures that data collected from varied sources are cleaned and enhanced before it enters the database. By making this a repetitive process for all incoming as well as existing data, the quality of data used by predictive analytic tools can be dramatically increased.
· Make data relevant
Apart from poor quality input data, analytics is also affected by redundant data. Even data that was accurate at the time of collection can become redundant over time and affect the output quality of analytic tools. However, putting a data strategy in place that checks data for relevancy regularly can help identify redundant data and ensure that only relevant data is used by the analytics team.
· Establishing trust
Security is another common concern for businesses that rely on data. Failing to protect consumer data can affect the loyalty customers have for the brand and put the company at risk of heavy fines. To prevent this from happening, companies need to define coherent and strong security policies that protect their data from piracy, corruption, etc. The company should be able to trace the flow of data, maintain its integrity and all the related metadata. This is especially important in the current remote working scenario.
· Encourage data sharing
Compartmentalized data collection and analytics is not beneficial for the organization as a whole. For the results of data analytics to be helpful in corporate decision making, data must be easily shareable within the organization to teams with relevant permissions. The organization may need to move data across one or multiple cloud platforms. A data strategy that supports open data stores and open compute architecture makes it easier for organizations to handle data and optimizes their cloud costs. Now, information can be shared and consolidated for better analytics.
· Build greater resiliency
Data does not restrict the structure or scope of data storage platforms but integrated data structures can cause linkage and relevancy issues. A data strategy promoting an enterprise data cloud can overcome this challenge. It allows businesses to ingest huge amounts of data and give our real-time analytics while complying with data regulations. It also allows them to apply various analytic disciplines to data regardless of whether it resides on a private cloud, public cloud, on-premise or hybrid platform. This is imperative to driving business decisions by data insights and building resilience.
· Optimize manpower
Choosing the right data management software is a critical part of forming a data strategy. The skill shortage must be addressed to ensure that data management teams are not overloaded. Using software that runs in the background to verify data such as customer identities, contact details, etc. helps improve data quality while minimizing the risk of human error and allowing businesses to optimize their manpower deployment.
A Final Word
Data analytics plays a key role in helping businesses of all sizes understand their customers, explore new markets and make decisions to optimize their functioning. In today’s world, data analytics can give businesses the most strategic advantage over their competitors. But, to get reliable data for such advanced analytics, every business must have a well-thought-out data strategy and data governance policies in place.
