Transforming data into useful information: the essential path to evolution
Data is a very valuable asset for organizations. Daily activity generates a large amount of information, but, Its mere existence is not synonymous with good use.
For data to become useful information for decision making, it is vital collect, transform and analyze them in a structured and strategic way.
Building a data-driven organizational culture is not achieved overnight. It is not a random process, nor is it imposed.
It takes time and must be supported by coordinated strategies, planning and execution anchored in deep business commitment. And it requires the creation of a data-driven culture, in which the entire organization trusts that Data is a source for making better decisions.
In this article we talk about data-driven organizations: what they are, how to incorporate data analysis at all levels, and the challenges involved in transforming culture. In addition, we delve into the advantages of artificial intelligence and machine learning to boost the Transformation of data into useful information for decision making.
What are data-driven organizations?
Minute by minute, people and organizations generate an incalculable amount of information. As an example, some statistics estimate that If you wanted to download all the information from the Internet, it would take more than 180 million years..
This information is a gold mine for business. It is not for nothing that data is considered to be the oil of today. If used well, it can be a decisive factor in the evolution of the activity.
Data-driven organizations leverage this advantage strategically. They implement systems and processes that ensure that All members have access to the relevant and up-to-date information they need. to make informed, fact-based decisions.
The importance of migrating to this type of business mindset stems primarily from the change in consumer behavior. Customers are increasingly demanding and demand faster and more assertive responses to their needs.
Therefore, organizations that adapt to these changes effectively, They are the ones that have the most chances of evolving and maintaining their competitiveness..
How to build a data-driven company
As we mentioned before, building a data-driven company culture takes time. It is a process that requires synchronized strategy, planning and execution..
First, the foundations need to be laid. All levels of the organization need to be aligned on the importance of data adoptionIn addition, a sense of responsibility and joint commitment must be developed, and clear objectives defined.
In short, it involves change the mindset of the organization. In fact, it is often culture, rather than technology, that is the obstacle to progress towards business transformation.
“In our experience leading data analysis projects with organizations from various industries, we learned that data is the past, present and future of organizations,” explains Mariana Oliva, Project and Process Manager at IT Patagonia.
“The past, because they are the starting point that shows us how we have been doing things up to this point. The present, because they allow us to see where we are today. And the future, because if they are well used, they can become the compass that guides us towards where we need to go to obtain the results we have planned,” he explains.
Being a data-driven organization implies Putting data at the center of attention and decisions. Through the trends and discoveries that data provides, business is driven and new innovative ideas emerge.
But to get to that point, Disaggregated data must be transformed into information.
As an intermediary between both states, there must be a systematic and strategic analysis process, supported by an integrative vision of the organization, in which each department is not isolated but synergistic with the others.
As a result, knowledge of the market and its objectives is improved, hypotheses can be anticipated based on management assessment, and risks are reduced thanks to predictive analysis based on reliable data.
How to incorporate data collection and analysis at all levels of the company?
Today, companies that focus on data analytics have advantages over those that don't. For example:
- Improve decision making
- Reduce costs
- Expanding profit margins
What is the best way to begin implementing these benefits? The process must start at the top of the organization. It is essential that senior management leads the data-driven culture, so that it then descends to the other levels of the organization.
Only when the executive line is clear about the objective and advantages of working on a data model, can the operational lines be involved in the importance of working, analyzing and making decisions based on data.
Transforming people: the most difficult challenge
As with almost all projects, the main complication when implementing new business analytics tools is the impact on teams.
Therefore, it is necessary Accompanying technological migration with change management strategies that effectively address the transition and transformation of objectives, processes and technologies, and help people adapt to change.
“In our experience managing digital transformation projects we have learned that it is essential to have clear and effective strategies and objectives involve all levels in the development of plans for each area and sector,” Oliva exemplifies.
In addition, it is important to be able to measure these results with data analytics and establish continuous improvement and self-assessment processes. Only when people feel involved in the entire process do they understand the importance of data analysis and it is easier to achieve change throughout the organization.
How to transform data into useful information?
Data alone has no special power. But when interpreted effectively and in the right context, lead to relevant and actionable discoveries to enhance business plans.
According to an article from Harvard Business Review, companies that prevailed in establishing a data-driven culture have in common skills such as: persistence, resilience and resolve.
As we mentioned before, the processes that are implemented have to be well studied and organized.
- The first step is be clear about what the objective is, that is, where you want to go.
- Once the objectives have been defined, it is necessary to collect the necessary data from all sectors of the organization and prepare them for analysis based on the objectives.
- At this point it is necessary Design a process that allows for a 360° view, in which information is made available to the entire organization.
- It is essential to have information well organized and structured to get the most out of it. Adapting to technological changes implies responsibility and security regarding the use of data.
- Then, it is needed Identify, develop and measure OKRs and KPIs over time.
- Finally, define the roles responsible for analyzing, debugging and modeling the data with the aim of finding valuable information.
One of the first improvements that data-driven companies see is the drastic reduction of human errors and biases typical of working with manual collection, compilation and analysis mechanisms. On the other hand, Access to information is democratized.
AI and machine learning in business analytics
Artificial intelligence emerged in the 1960s and sought to make computers try to imitate the intelligence of people. In recent years it gained momentum and became a topic on everyone's lips, thanks to popular generative artificial intelligence tools such as ChatGPT, Dall-e, Bard, SlidesAI and Copy.ai.
Today, it is a technology that can play a key role in driving and evolving the way business information is analyzed and created.
Taking advantage of machine learning - a branch of artificial intelligence that focuses on the use of data and algorithms to imitate the way humans learn - Computers are learning from our data and showing great potential to help us Reduce project time from months to just weeks or days.
The main advantage of machine learning? It requires minimal preparation and can be adapted to specific use cases with very little sample data.
Recently, IT Patagonia signed a partnership agreement with Dataiku, a collaborative data science platform that stands out for providing a collaborative work environment in which People of different technical levels can operate together, improving experience and maximizing results..
Its agnostic approach allows it to adapt to any type of statistical language (Python, Impala, Escala or others) and any type of database, for drastically reduce development times.
What is the difference between this type of platform? Mainly, the fact that all team members can Take advantage of the organization's data, without knowing how to code.
The collaborative environment attracts people of different technical levels to work together, opening the doors to non-technical teams to adopt the data-driven mindset that will allow them to evolve.
5 Dataiku solutions for the financial ecosystem that can be quickly implemented
The financial ecosystem is not exempt from the change in consumer behavior that we mentioned at the beginning of this article.
To achieve their strategic objectives, remain competitive and successfully respond to the demands of their users, financial institutions need to be increasingly efficient and disruptive. The speed of implementation and assertiveness of solutions is essential.
In response, the solutions pay and play based on data analytics and artificial intelligence allow accelerate the development, research and analysis of customer behavior, with the governance and explainability functions necessary in the current regulatory environment.
The following are five usage examples.
Smart customer segmentation
To understand user behaviors and preferences and segment them. With this knowledge, services can be personalized and recommendations made to enhance the experience.
Credit Score
AutoML (automatic machine learning) makes it possible to accelerate the credit scoring process and implement higher-performance models to achieve accurate event prediction.
Fraud prevention
Integrating machine learning into fraud detection optimizes the detection of fraudulent behavior, limiting the impact on losses and improving customer confidence.
Financial forecast
Generative AI makes it easier to create more accurate and less expensive forecasts, with immediate results.
Service Coverage Analysis
Spatial exploration of the distribution network through the collection, processing and analysis of data from technologies such as GPS, location sensors and satellites improves coverage and competitive positioning strategies.
Conclusions
Data is a very valuable asset for organizations, and it must be collected, transformed and analyzed in a structured and strategic way so that the data becomes useful information for decision making.
Changing the mindset across the entire organization is the main challenge when implementing business intelligence. For a successful outcome, technological migration must be complemented with change management strategies.
Through machine learning, computers are learning from our data and showing great potential to help organizations reduce project time from months to just weeks or days.
Contact us for learn more.
