The Data Driven Journey to Analytics Maturity

This post will explain what the data-driven journey is and show you the steps your company needs to take to achieve analytics advantage.

Companies have more and more access to information capable of generating revolutionary ideas, solving old problems and leveraging business results. But in a highly competitive market with replicable technologies, they need to differentiate themselves.

How to do it?

One of the alternatives is to invest in high-performance processes to deliver goods and services efficiently, reduce risks, and increase your competitive advantage over competitors.

And that's what companies with high analytical maturity are doing.

Analytics maturity as a competitive advantage

Numerous studies claim that companies can benefit from a more analytical decision-making process.

And the companies that stand out the most in the market invest a lot in it.

But after all, what is analytical maturity?

Companies with analytical maturity are those that incorporate data analysis in a systemic way, in all their departments.

They extract the potential retained in your information through data management, statistical and quantitative analysis, and apply that information to your decision-making.

In these organizations, the principles and tools of data science and analytics are not used in a one-off manner. They are part of the overall strategy and culture of the business, so they are present in everyone's daily lives.

Now you might be wondering: who are they?

Companies with analytical maturity are spread across a variety of industries. However, some industries are more receptive to the data-driven culture.

The ranking of industries leading the analytics revolution, prepared by the International Institute of Analytics in 2016, proves this:

Amazon, Uber, Google, and Netflix are examples of "natively digital" companies.

From the graph, it can be seen that they have a very advanced analytical maturity in relation to other industries.

Do you know why?

Despite operating in different industries, digitally native companies share some attributes in common:

1. Already born digital
2. From the outset, they consider analytics as a competitive advantage
3. Develop and implement data analytics across all their departments
4. Have the support and commitment of senior management
5. Have a well-established data-driven culture

But this is not the case for most companies.

Most organizations don't have analytical capabilities or a well-developed analytical plan.

In a Gartner survey, nearly 91% of the 196 organizations surveyed globally say they have not reached a transformational level of data and analytics maturity, even though this area is an investment priority for CIOs.

This occurs for several reasons, including:

• Structure unprepared for data analysis
• Lack of resources
• Little investment in appropriate technologies
• Lack of trained professionals

In fact, there are several difficulties inherent in the process of developing analytical maturity. There are many pieces to fit together, investments in technologies, changes in processes, breaking paradigms in the organizational culture...

If you feel an urgency to adapt to such a competitive market and recognize analytics as a crucial skill for your business, you're already on the right track.

But remember: resilience is the key to your analytics success.

You can't promise a data-driven journey without challenges. After all, it's a continuous iteration process. In fact, even highly analytical companies have a lot of room for improvement in terms of analytical skills. Still, we can all learn from them.

What is the Data Driven Journey?

To become an analytics company, you first need to understand where you are and then define where you want to go.

We know that this process can be challenging. That's why we've created the data-driven journey, a complete roadmap to help you identify the analytical maturity of your business.

This path is divided into 5 stages that encompass the skills and challenges of each stage of the journey.

They are:

1. STEP 1 - Pre-release
2. STEP 2 - Launch
3. STEP 3 - Zero Gravity
4. STEP 4 - Propulsion
5. STEP 5 - Interstellar

And each of the stages has different characteristics regarding these pillars:

1. Data
2. Technologies
3. People
4. Processes

Navigating the Data Driven Journey

Now, let's summarize all the steps, point out their differences and characteristics for you to understand the entire path of the data-driven journey!

Step 1: Pre-launch

In step 1, good practices or data-driven strategies are not well defined or have not even been created yet.

Here, companies even have some kind of analytical tool or application installed, but in general, they are resources used at the departmental level, which guide individual strategies of different sectors and do not communicate with other departments.

Therefore, they are of little relevance to the company's competitive strategy.

Key business features in this step:

• Data - Inconsistent, low-quality, and non-standardized; little explored, only for one-off reports within specific departments.
• Technologies- CSV, control sheets, Excel, text files. Basic tools for descriptive analytics. Decentralized databases, incapable of holding a massive volume of data.
• People – Few data-driven technical professionals. There are no enterprise-level analytics processes.
• Processes - Manual extraction of information for punctual analysis, by department, without processes defined by top management.

Step 2: Launch

In step 2, there is an advance in the use of analytics and data, but these technologies are not yet a priority, nor are they seen as a competitive advantage.

In these organizations there is a localized approach to business intelligence, but there is no cooperation between departments regarding the use of data analytics and data is still treated individually.

Key business features in this step:

• Data - Usable, but retained in repositories and databases under the control of specific departments. Data isolated from the rest of the organization.
• Technologies- BI and basic analytical tools. Poorly integrated systems, used by specific departments.
• People- Data strategy is not yet a priority for executives. There is no integration of information, and departments deal with their data individually.
• Processes- Manual or automatic extraction of information located by department, without cross-referencing between areas.

Step 3: Zero Gravity

Companies navigating this stage already understand the importance of data in business. In this way, they are focused on promoting efforts to structure and centralize their data.

Here, the main focus of organizations is to understand how they can organize their data structure to improve their performance and increase market value.

Therefore, unlike the previous steps, there is more commitment from leaders and investment of resources for the development of analytical intelligence.

Key business features in this step:

• Data- Centralized and unified across a data warehouse or on-premises.
• Technologies- Basic statistics, segmentation, querying and reporting in databases, integrated analytical tools.
• People- Leaders and senior management recognize analytical skills as a competitive advantage.
• Processes- Creation of a specific department or process focused on analytics. Data infrastructure organized with ELT or ETL.

Step 4: Propulsion

In propulsion, there is the development of analytical capabilities across the organization.

These companies routinely use interactive visualizations and BI dashboards to make data-driven decisions and solve business problems. Its processes are focused on the present, with data visualization tools and preparing for the future.

In this way, they are highly data-driven organizations that have all the knowledge and structure necessary to take the next step. However, they have not yet reached there due to obstacles such as the lack of introduction of analytical competition as a business strategy, for example.

Key business features in this step:

• Data - Integrated, consistent, centralized in integrated repositories. Data connected to a single BI framework with a holistic view of the business.
• Technologies- Basic predictive analytics, complete BI systems, report automation, automated market intelligence reporting.
• People- Leaders and senior management recognize analytical skills as a competitive advantage. There is progress, but at a slow and sometimes unsatisfactory pace.
• Processes- Good practices and well-defined analytics processes by senior management and followed by the company's departments.

Step 5: Interstellar

Here, companies use analytics to the fullest, as a business strategy to stay at the forefront of the analytical competition.

They receive support from senior management, have a structured data-driven culture, and an efficient analytics approach applied across all business departments. In addition, they incorporate the data-driven decision-making process at all levels of their structure.

These companies reap the rewards of those at the forefront of analytical competition. However, they are not stagnant and comfortable. They are resilient and focus continuous efforts to revamp analytical strategies when necessary.

Key business features in this step:

• Data - Integrated, high-quality, and easily accessible. Data seen and used as a strategic asset.
• Technologies - Predictive and prescriptive analytics, end-to-end BI, predictive modeling, machine learning, what-if models, AI-powered process automation.
• People- All departments use advanced data analytics, and all decisions, from the simplest to the most complex, are grounded in data.
• Processes - An analytics approach implemented throughout the company, with a comprehensive viewof past, present and future information.

How to take the next step?

Adapting to analytical processes and behaviors is like any organizational change: it requires continuous efforts and a lot of patience, but it is possible and brings incredible business results.

To start a project, the first step is to identify where your company is and thus define where you want to go. In addition, it is essential to evaluate the impact of the intended solution, analyze its value and complexity, and then calculate the return on the intended investment.

But don't forget: there is no rule or cake recipe in the data-driven journey.

Each company has its particularities and a more suitable path to follow. However, experience and understanding in the area is essential to accelerate the journey and reduce the cost of learning for those who want to be analytical.

Still not sure how to get started? We can help you with that! We specialize in putting companies at the top of the analytics revolution.

Get in touch today and find out what we can do for you!

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