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Data driven

Tomar decisiones basadas en información y análisis de datos

Table of Contents

What is a data driven company?

Strategic concept that can be applied to any business. In practice, it means making decisions based on the analysis and interpretation of data. This term can be translated as data-driven management.

Being data driven means making decisions based on information and data analysis instead of being guided by intuition, emotions or our own opinions.

It also means considering that thanks to new technologies and software innovations, it is much easier for companies to access and collect massive data and use it to predict behavior and make more effective decisions. This information and figures are known as Big Data and represent a development engine for those companies that have adapted their business culture to a digital and data-driven environment.

Benefits of Data Driven

Consistency

Agility

Transparency

Data

Costs

Income

Decision

The use of key data in all major decision-making processes ensures that a company achieves consistent results. Important people and market trends may change, but if you’re a data-driven organization, this won’t affect how key decisions are made.

Agility is what enables organizations to turn rapid changes into opportunities and avoid disruption by responding nimbly to competitive threats.

To achieve business agility, we will have to be prepared to respond to unexpected changes in business and competitive environments through the creation of innovative solutions, and thanks to data, we can find solutions in information (data) and its good analysis.

Information is an incredibly valuable commodity in data-driven organizations. Therefore, it is required that the relevant information is available throughout the company and all departments, to be used.

Collaboration of this kind ensures that everyone pulls in the same direction. It fosters loyalty and responsibility as each member of the team knows exactly what is going on and what their role is. There are fewer errors, as communication failures are less likely to occur. Additionally, employees are more likely to suggest improvements and positive changes because they have a complete understanding of the company’s current position and long-term goals.

Data-driven organizations collect huge volumes of data over time. Much of this data is, in effect, feedback that provides insight into what customers like and don’t like. This information is useful for quick wins, but it can also be the basis for long-term improvements.

With so much data, it’s easier to spot patterns as they emerge and identify gaps in the products or services you offer. Design new products or transform existing offerings based on the data you’ve collected and drive growth as a result.

A Data Driven company can use the data to identify possible cost reduction measures in all areas of the company. The data may reveal that a specific product is returned more than any other, for example. With this information, you can evaluate your product, identify and address issues customers are having with it, and reduce unnecessary administration and shipping costs.

The more effectively data is used in decision making, the more agile your organization will be. This agility enables data-driven organizations to outperform their competition and increase revenue. The most forward-thinking companies will use this revenue to fund innovations and target new markets, in turn unlocking new revenue streams and driving further growth.

Indecision is the enemy of progress. While you are deliberating and deciding, your competition has already taken steps to get ahead of you. Without hard data to support your decisions, projects can be slow to move forward, especially if there is disagreement or a conflicting point of view.

Data-driven organizations can move forward much faster and with greater confidence in their decisions. They have hard, cold evidence to back up their ideas, so a long debate isn’t necessary.

The “gut feeling” aspect of business decision making has not been lost. It just means that now someone can look at the data and make decisions based on both the information in front of them and their own experience.

Is Big Data the same as Data Driven?

Big Data vs. Data Driven

When talking about the use of data in the business environment, and its application in the business, there is a variety of concepts that generally have similarities and cause confusion. In this case, despite the fact that Big Data and Data Driven have in common the use of data to understand patterns, they are not exactly the same.

On the one hand, we have Big Data, defined as the set of massive data extracted from different sources; while Data Driven represents the business model, or the decision-making methodology, that uses this data with the aim of improving the operation of the company or a specific action.

Data Informed vs. Data Driven

Data Informed companies collect and use data, but only through simple and dynamic techniques based on data, with a mainly informative and expository function, for example graphics. While data-driven companies use algorithms in decision making, which would be more linked towards management due to their influence on the direction of the company.

What are the most data driven sectors?

Banking Industry ---
Insurance Companies ---
Telecommunications operators ---
Health ---
Automotive industry ---
Manufacturing ---
Retail ---

The use of data for decision making varies depending on the sectors rather than the geographical location of the company. According to the aforementioned study by the Capgemini Institute, the most data-driven sector is the banking industry (65%), insurance companies (55%), telecommunications operators (54%), companies linked to health (53%) and the automotive industry (51%). On the other hand, we find the manufacturing industry and retail, both with 43%.

However, and as expected, the large contemporary technology companies such as Amazon, Netflix, Google or Apple are the ones that have the most advantage in the data driven culture and serve as a clear example of how effective it can be to base decisions of the company in the data.

Data Value

Data-driven organizations are striving to base their strategic business decisions on the evidence provided by data, which requires a certain rigor while at the same time the ability to innovate based on identifying – within the data – opportunities that may give rise to new products or markets.

A. Data Driven as a Competitive Advantage

A1. Data as Financial Asset

Data driven is characterized by using data as an asset to increase business efficiency, since based on information from the past – data – it creates projections.

It is useful to think of data as having business value as a kind of financial call option, that is, they give us the opportunity to make changes in the supply chain or launch a new product, but they do not force us to do so, since we can exercise the option or not.

(Graph on increased efficiency from DD implementation)

A2. Data is the engine of the reinvention process

Data-driven organizations treat data as an asset, not the property of individual departments. They establish systems to collect, store, organize, and process valuable data and make it securely available to the people and applications that need it.

To collect, store, organize, and take action on their information, these organizations consolidate data into centralized data lakes for easy discovery, governance, and access. They also use technologies like machine learning (ML) to unlock value from their data, such as improving operational efficiency, optimizing processes, developing new products and revenue streams, and building better customer experiences.

Machine Learning and its application in Data Driven

In the physical world, where most things that humans interact with are based on dynamic relationships with various intangible data, the human mind performs many simple data-driven calculations every day.

In the same way, with data stored by the company or labeled training data, through Machine Learning, we can receive data-based feedback on what are the best solutions for our business decisions.

Machine learning thrives in data-rich environments. No matter where you are in your digital transformation journey, machine learning can help you better manage, make sense of, and leverage your data. The result: smarter decisions Smarter decisions that advance your business goals, such as increased productivity, efficiency, or safety.

Diagram of relationship between ML and DD

Main challenges in the implementation of Data Driven

More accessible data: Data is often locked away in transactional and relational databases and not readily accessible, as privacy is thought to be promoted by making data as inaccessible as possible.

Companies may not have the right analytical tools, or these may not be available to the right people at the right time.

Objectives for a good implementation of Data Driven

Maximize data availability, respecting privacy and confidentiality.

Foster transparency across the company by breaking down information silos.

Give employees the right tools to explore data unexpectedly and taking advantage of the latest advances in analytics.

And make sure you have the knowledge to interpret the data, both rigorously and creatively.

Focus your company towards Data Driven

Five steps to reinvent yourself as a data-driven organization

Research how data flows in your organization and what types of access control exist. Uncover data silos and measure how difficult it is for employees to access the data they need.

Verify that a senior, respected and empowered leader is driving the cultural initiative for the company to become a truly data-driven organization.

Treat data as a product, in part, by bringing application engineers and data engineers together. Tightly align data, product, and integration strategies.

The importance of IT. IT brings a unique view of the entire business cycle, interdepartmental workflows, and transactional systems that contain valuable information.

Create a structure for data governance that gives employees more freedom instead of limiting them.

Change of culture and processes

Going data-driven, in this sense, requires a very different way of making decisions; it is a profound cultural change for many organizations. In the past, we could make decisions by drawing up detailed plans, analyzing the options with the available data, and choosing the option that – given only the available data – seemed to offer the best results. In the digital world, we refuse to accept only the data available at the time the plan is created. Instead, we design experiments to obtain additional data and then incorporate that data into our decision making. We resolve uncertainty by generating new data.

How to become a data-driven organization?

  • Create a culture of innovation that puts data at the heart of your business strategy
  • Build data capabilities to help drive that culture

Engage in data-driven decision making

Organizations need to use data to guide and justify decisions continuously. This starts at the top with sustained, dedicated engagement. Pick an established, respected executive as the ‘single-threaded leader’ of your data initiatives who can push this focus forward.

Educate everyone

A data-driven culture is only fully realized when data analytics skills are common across roles in your organization—and not exclusive to just data scientists. Leverage storytelling to ensure a seamless translation between the ‘science’ of data and the ‘art’ of business when championing best practices and sharing out business wins.

Eliminate data blockers

Three common data blockers stand in the way of becoming data-driven:

Old way solutions: When you change the processes and products to be data-driven, don’t make these changes optional.

Resistance solutions: Create a culture that uses data to seek an honest inquiry to help EVERYONE be better.

Silo solutions: Treat data as an organizational asset, not a departmental property.

Soluciones aisladas: trate los datos como un activo de la organización, no como una propiedad del departamento.

Enable frontline action

Democratize the ability to act on data and have the confidence to push decisions down to the frontline, where data and insights reside. Stop relying on the HiPPO (highest paid person’s opinion) to embrace decentralized decision making.

How can AWS help you leverage the power of data?

With a better understanding of data and how to best leverage it for your company, the keys to unlocking new opportunities are in your hand. At AWS, we’re always working from a ‘Day 1’ perspective to ensure we’re promoting—and helping others benefit from—the power of being data-driven.

AWS Services Related to Data Driven

Amazon Relational Database Service (RDS): Service to provision relational databases

Database | Customers

Dynamo_DB

Amazon RDS for MySQL or MariaDB

Log events

Supports the following engines: Aurora (Aurora MySQL y Aurora PostgreSQL) | PostgreSQL | MySQL | MariaDB | Oracle | SQL Server
Aurora: Relational Database, Amazon’s version to be used in the cloud, is faster than the standard versions and offers greater availability options

AWS S3 (Simple Storage Service)

  1. Unlimited Object Storage.

  2. It is used to store files, websites use it to store static files, such as: Images, videos, CSS, JS files, etc.

Morris Opazo - Amazon Simple Storage Service (Amazon S3)-02

In S3 there are 7 storage classes.

Amazon SageMaker

AWS service capable of creating Machine Learning models. Here you can develop a strategic business model that, based on the organization’s data, delivers solutions.

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