The Business Data Analytics Journey

With over two decades of working with clients to maximize their use of data to gain business insights, I have found that there is a common Business Data and Analytics Journey they embark on. The journey involves understanding where they are currently within that cycle and where they want to go to maximize their effectiveness using data to enhance their organization. Organizations that travel along the Business Data and Analytics Journey find that they achieve the ability to use data insights to drive the end outcomes they desire.

Collect the data:

It is impossible to drive insights from data if you have none. Collection of data is where the journey begins. The key is to collect the right data in the most practical and efficient way possible. Data is information about any object. Most of the time you are already collecting this data during your everyday business operations. The data could be quantitative such as financials, sales, and inventory numbers; or the data could be qualitative such as a customer survey or an observation. You will also need to determine if you already have this data or need to collect the data from other sources. Below are the various sources to consider:

First-Party Data – Data your organization collects.
Second-Party Data –Data that is not your data, but is another organization’s first-party data
Third-Party Data – Data collected and aggregated from multiple sources by a third- party organization.

It is important to also know the data type, which could be structured (e.g., within rows and columns) or unstructured (e.g., pictures, videos, emails, social media posts).

In this stage of the Business Data and Analytics Journey you could be collecting data and then pushing data into systems/applications. For instance, you may be collecting data within a web application that then is storing the data in the application’s database. You might then push that data using application integration techniques to another application. In this stage of the Business Data and Analytics Journey you may be conducting application integration, but not data integration. The main distinction between application integration and data integration (which we will talk about later in processing the data), you are using the “raw data”, meaning you are not changing the data from the way it was entered into the system/application.

Process the data:

Once you collect the right data you need to process that data and get it ready for analysis. Processing the data refers to the activities involving aggregating, cleansing, and preparing the data. Data Analyst/Data Scientist use a variety of techniques such as Extract Transform Load (ETL), data wrangling, and data integration. These techniques often overlap as the Data Analyst/Data Scientist is aggregating data from disparate data sources, cleansing data to ensure quality of the data, and preparing the data in a way that it can be used for analysis.

Centralize the data:

Centralizing the data is aggregating and storing data in a secure way so that the data can be used for analysis. Typically, this involves moving the processed and sometimes unprocessed data into a database, data warehouse, data lake or a data lakehouse. Below are definitions of tools used to centralize the data:

Database – A systematic way of collecting data so that the data can be accessed, analyzed, transformed, and stored in such a way to be queried for efficiency.
Data Warehouse – A system designed to store and optimize the querying of structured data used for reporting and data analytics.
Data Lake – A system designed as a central repository for all types of data both structured and unstructured.
Data Lakehouse – A system designed to be a combination of both a data warehouse and a data lake

The best repository for the data is determined by what type of data, the type of analysis that will be conducted and how many users will be consuming the data for analysis. For instance, if you have structured data and you are building dashboard reports that start with Key Performance Indicators (KPI’s) and drill down to detail information about the KPI for say 30 users, then a data warehouse is probably your best option. The data warehouse is best for structured data and is designed for querying data quickly which is great for Business Intelligence (BI) drillable dashboards.

Analyze the data:

Once the data is processed and centralized, it is ready for analysis. The next step is to consider the type of analytics you need to conduct. Usually, there is a business problem or pain point within the organization that is driving the need for knowledge. Defining what business problem, you are trying to solve or the insights you wish to gain is the first step. Don’t be surprised if you find other kernels of information along the way. Additionally, data scientists conduct data exploration, which uncovers information that you might not have thought of our even knew existed.

Data scientists will use a variety of techniques to analyze data such as statistical modeling, classification, regression, clustering, decision trees, neural networks, anomaly detection, association rules, natural language processing, DataOps, MLOps. The type of data analytics performed will fall into the four categories listed below:

As your analytics process continues to become more advanced you will gain value with what you learn and how you can apply those insights. Going from understanding of what happened in the past to what course of action you should take to make an action occur is the ultimate prize. With value does come more complexity and having the right data analytics team is essential to handling that complexity.

Achieve insights from data:

As you conduct your analysis the intermediate outcome is to gain insight from the analysis you are conducting. It is important to note that analytics is an iterative process, meaning as you gain more insights, ideas, and data, you continue to revise your analysis to gain better results. For instance, a descriptive analytics process of displaying BI reports gives you wonderful insight into what happened in the past. However, you then might be asking “why did this happen” or “how can we predict what will happen in the future”. As you then start to build predictive models you will find they need multiple iterations to refine the analysis before a predictive model really is correct. And over time that model needs to be updated based on new data. Don’t be deterred by this consequence, in fact, embrace it. The more you learn the better you will be able to deploy those insights in action for your organization.

Deploy insights into action:

Now that your analysis is complete it is time to deploy your insights into action. Deploying what you have learned is more than just sharing the raw results of the analysis; it is interpreting the analytic outcomes and deploying them in a meaningful way within the organization. Does the outcome change a process to gain more operational efficiency? Does the outcome give you insights into why you are losing clients? Does the outcome open your eyes to a new business segment or audience you did not know you had? Whatever the outcome, it is important that you deploy that insight in a meaningful way within the organization, otherwise you just spent a lot of money on a science experiment.

Conclusion:

There is value in every stage of the Business Data and Analytics Journey. Utilizing data to guide decisions enhances the ability for a company to scale and grow. Accomplishing some of these objectives might seem daunting but I promise you it is worth the effort. Understanding your business, increasing your efficiency, and anticipating opportunities, enhances your ability to grow a successful division or company. Are you ready to make data-driven decisions? Contact us at Scalesology and let’s together ensure your business scales with the right data insights and technology.