Introduction
Data is not yet strategic for many companies. It is evident from the success stories that data can be of great value. Many organizations incorporate data into their business strategies and tailor their data efforts to business transformation requirements. Data can help you understand and improve your business processes, reducing wasted time and money. All companies are feeling the impact of waste. It consumes resources, wastes time, and ultimately impacts revenue.
Importance of Data
When properly integrated, data can catalyze many business strategies by enhancing business processes and enabling people to execute them. Data integration starts with examination of complexities within the organization and setting priorities that are agreeable by all. Leaders, managers and data professionals focus on considering data and strategies via 6 value modes- improved process, competitiveness, products, human skills, risk management and incorporation of data into products and services.
Companies are ‘data rich’ but they struggle to integrate data into their business strategies. From lack of talent to unreasonable cultural expectations, there are many reasons to cite. It is essential for organizations to solve these problems for tapping the power of data. The types of data that companies collect can be divided into five major categories: business processes, real-world observations, biological data, public data and personal data.
Even though businesses are complex, data is not strategic to many organizations yet. It is important to cater to the needs of customers, drive out competitors, close gaps in talent or qualification voids and take into account uncertain regulatory framework conditions while defining a corporate strategy. Data can be of tremendous value, but it is difficult to know where it goes.
The types of data that have proven to be most valuable to businesses are customer data, IT data, and internal financial data. Managers use datasets everyday across organizations around the world. Centralized data management is of not everyone’s interest at organizations, whereas they ponder when problems arise in the form of data possessing unexpected risks. Companies acknowledge that privacy and security are crucial. For developing a data driven organization, significant cultural adaptation is needed which is certainly time consuming. No wonder, data is a distant strategy compared to mainstream business strategies.
Data Driven Business Strategies
In a transformative business strategy, data-driven intelligence must be “easily accessible, interpretable, and practical” as and when needed. At the macro level, a data-driven culture needs to be consciously disseminated end-to-end throughout the organization.
Digital companies recognize the importance of data availability and incorporate this culture into their business models to stay competitive. Late movers in the data culture can quickly adapt to data-driven business strategies by aligning core business goals with the goals of the company’s data strategy.
Data-driven business strategies ideally combine data science best practices with business best practices to reduce efficiency, performance, productivity, profits and costs. At the micro level, data technology uses feature-specific KPIs to drive decision-making in all business functions such as human resources, finance, operations, and marketing.
Here are some important considerations when building a data-driven business strategy:
- Data and Business Impact Link: If the purpose and use of business data is directly linked to core business goals, there may be useful or profitable data. In many cases, leaders, managers and employees cannot map the available data to actual business requirements. As a result, even the best-planned business strategies do not produce the expected results.
- Strategic Management buy in of Data Policy and Program: This can only happen if the data is clearly linked to the realized business effect. A management buy-in strategy needs to go through several cycles of persuasion, conversion, setbacks, and renegotiations. If the buy-in strategy is not carefully designed for all levels of employees, the wider business users in the organization will not be able to easily adapt the data-driven culture.
- Data Use Cases to be Tested Regularly: Once tested, use cases can lead to better planning changes and even business strategy changes. Unless your data case plan is regularly monitored and tested against real-world business conditions, it can be difficult to bridge the gap between desired and real-world results.
Conclusion
Data side of business is not as complicated as many think it is. There are good opportunities and requirements with data driven business analytics, artificial intelligence, data protection, data quality, process automation, monetization and small quantities of data and security. This being the fact, still the majority of data work is associated with feeding new database inputs, matching non-speaking systems, implementing business intelligence systems, data to feed machine learning algorithms, establishing low level governance and defining metadata.
