The context of the beginning of the year is the ideal time for SMEs to incorporate a Big Data strategy that allows them to gain competitiveness and profitability.
If the digital acceleration generated by the pandemic reached a large number of Spanish SMEs, many of them still need to fully undergo a digital transformation focused on the massive and coherent management of large volumes of data. The truth is that most of them are committed to digitalization: the 70% of companies of this type trust in digitalization and is already considering investing in it in the next three years.
What makes a Big Data strategy useful for this type of company is that it often provides answers to questions that not even the organization itself had previously asked itself. After analyzing the information, organizations can proactively focus on their own problems and, at the same time, take advantage of them to generate new opportunities, more efficient operations, greater profits and customer loyalty.
Many times, the 5 Vs of Big Data (Volume, Velocity, Variety, Veracity and Value) usually lead SMEs to face the challenge of knowing if they are capable of extracting real, high-quality data in large groups of massive, changing data. and complex.
It often happens that SMEs come across many sources and types of data and the difficulty in integrating them increases. Data sources can range from digital marketing campaigns and social networks, to third-party data, including data from IoT sources, structured data (CRM/ERP), unstructured data (documents, videos, audios, etc.) and semi-structured data. -structured (spreadsheets, reports).
On the other hand, faced with a large volume of data, it becomes difficult to achieve efficiency in a short time: collecting, cleaning, integrating and obtaining a high and truthful quality of data quickly when the data changes at speed requires high processing power to avoid thus draw conclusions from erroneous information.
Faced with this scenario, SMEs must address 4 instances that guide a data strategy:
- Definition of a problem: Translate the business problem and identify data sources. Be very clear about the problem you want to solve, so ask yourself: What is my main objective? What business problem do I have? What do I want to explain using data?
- Data preparation: Select useful data and extract it from its sources. There is a central question here: How much customer history do I have saved? Who is the owner of the data? Big Data concentrates data from numerous different sources and applications. Conventional data integration mechanisms are generally not up to this task, which is why new strategies and technologies are required. During the integration process it is necessary to incorporate data, process it and ensure that it is formatted and available.
- Data storage: Big Data requires secure data storage that also has the necessary processing requirements and processing engines. The cloud as a place to store data is progressively increasing in popularity, because it is compatible with the technological requirements of Big Data and is accessible to SMEs.
- Data study: Analyze the variables to understand their behavior and relationship. A data-oriented culture requires systematic decision-making based on a “cult of data.” Investment in Big Data for SMEs is truly profitable when data is analyzed and used appropriately, exploring new opportunities and building data models based on machine learning and artificial intelligence. Once the model is built, it is possible to predict reality from any available information.
The beginning of a new year is the ideal context to launch a digital transformation and Big Data strategy in SMEs; It is the first step in a process of profound disruption in a complex and changing context that requires from these companies a radical change in mentality and strategic approach.
Julio Cesar Blanco – January 10, 2023
