Companies are challenged to convert information and analytics into assets to generate value and achieve a competitive market difference.
The digitization of all our actions generates digital records that can be used to personalize content and experiences, design innovative products and services, improve offers and relationships, increase loyalty, and attract and capture new customers or users. This remains the basic premise of the power of data: analytics and big data.
Most companies do not get the desired value based on what they invest in that land1. They do not use the information to improve their efficiency and competitive position. They also do not have the technical capabilities to integrate, analyze efficiently, and use big data or lack a clear and solid strategy to leverage it as a competitive advantage.
Those who have achieved it manage to innovate with greater speed and forcefulness, streamline their business processes, sustainably increase profits, and reliably reduce operational and financial risks. Organizations that understand the power of data as strategic assets systematically seek to use information and analytics to optimize their decisions and continuous learning within a culture focused on producing insights.
Adopting a strategy focused on data, information, and applied intelligence requires facing three major challenges:
- The lack of a comprehensive business strategy recognizes the power of analytics for differentiation and competitiveness. With effective leadership commitment and concrete bets, there is no clear idea to forge a culture based on data analytics to make decisions.
- Lack of access to reliable data: if the energy of the organization is dedicated to integrating, cleaning, and enabling data that should be thought of as a valuable asset from its origin and protected in all processes; then not only will there be no confidence in decisions based on available data, but also analysis that can add differential value to the business.
- Complexity and slow access to dispersed and fragmented sources: it is common to find obstacles when managing internal and external databases growing exponentially. Reliance on rigid technology platforms and systems management paradigms that are not intended for agile and flexible analytics often makes business aspirations more expensive and difficult.
Addressing these challenges and achieving a data analytics-centric transformation involves focusing on four fundamental pillars.
Build a solid foundation
First, it is necessary to think of a platform created from a complete map of the sources, characteristics, uses, and peculiarities of the data, as they contribute to the business. Under this premise, an agile “supply chain” must be established, which provides adequate quality information and at the appropriate time to support a decision or action of value for the company.
Besides knowing the necessary data flow and analysis, it is also required to build and automate it in an open, flexible, and scalable technological environment.
Subsequently, a data architecture must be thought about. If there is a platform that integrates information generation and analysis processes appropriate to business needs, we understand and have designed models to identify, interpret, combine, transform and reuse them in different contexts, expanding potential use cases. Also, standardization, reliability, and robustness are guaranteed both for engineers and data scientists and for end-users who must apply them in their business decisions. Ensure the quality and reliability of the information and the ability of organizations to build insights. To generate business value in multiple contexts, it requires starting from an adequate master operating model (people, processes, and technology), aligned to the generation and maintenance of the quality and integrity of the information in all the processes where a given data originates, modifies, complements, uses and discards.
Protect, feed, and grow the strategic value of analytics
Companies require a formal commitment to achieving information governance by creating and maintaining a culture, functions, processes, and rules that allow it to be executed, controlled, and managed. In the same way, metadata standards are required to facilitate their understanding through robust and extended technical and business documentation, applicable to structured and unstructured data, fundamental aspects for trust and agility. The systematic consideration of the risks and the context ensures the veracity and validity, reflected in an increase in its value as a strategic asset.
Convert data into insights
Creating an analytical strategy requires establishing a vision and setting clear objectives to guide the analysis to generate value and provide the organization with an operating model that links analytics experts with business users, and these in turn with business users. Technology resources that enable access to data and the construction of assets to exploit it.
Of course, the techniques and applications available for advanced analysis, algorithm construction, and processes leveraged in machine learning and artificial intelligence should be used for all kinds of use cases. However, the primacy of the business and the generation of value must always be present, where the visualization layer of insights and the consumption of data and information are aligned to the expectations and habits of business users, always seeking to interpret trends, patterns, and predictions within the context of the industry.
Such integration is not achieved without the empowerment and amplification of human capabilities. The correct balance between the predictive and prescriptive power of algorithms and people’s ability to interpret enables the support and scaling of analytics-based solutions that provide to the business and its people.
Create value through information
Considering a data monetization strategy (direct, by selling it as a product to a third party, or indirectly using it as insights to improve supply or business efficiency) opens up disruptive opportunities in all industries.
The concept of “high performance” cannot really be understood outside of a data-driven decision-making context.
Of course, the culture of the organizations that aspire to this is open to information, and decisions are based on quantitative evidence. Employees have access to amplify the results of their daily work.
Companies that have competitive advantages based on data are those that:
- Design measurable and verifiable objectives.
- Generate specific metrics.
- Have the discipline to obtain information that ensures maximum value capture from business initiatives and decisions, a key aspect of the organization.
In the same way, they discover on time new opportunities or recognize deviations in the drawn plans.
Being genuinely data-driven leads organizations to differentiate themselves by maintaining sustainable capabilities. By design, they are “smart” (they use analytics to make better decisions), turning information into assets that increase their competitiveness internally and externally.