The Perfect Data Strategy for Improved Business Analytics

Advancements in AI and Machine Learning have given rise to data analytics’s growing importance and, therefore, data itself. Unless you have established the pre-requisite data collection steps, data storage, and data preparation, it is impossible to make a move to the data science process.

At Allwyn, we believe that the journey towards improved operations and decision-making starts with establishing a good data strategy and establishing the tools and processes required to easily analyze your enterprise data. This involves starting with your data discovery and data collection, organizing the data in a data warehouse or a data lake, and finally using Machine Learning to perform deep data analytics to enhance productivity, launch new business models or establish a strong competitive advantage. We have an established data life cycle process that starts with data discovery and ends with reaching business outcomes through Data Analysis, Machine Learning, and AI. We employ a two-phased approach to data transformation and operational transformation, as shown below.

In the first phase of data transformation, our goal is to design, build and maintain an enterprise data warehouse or a data lake. This helps in making the most of an organization’s valuable data assets, break down data silos, and create a data maturity model that helps accelerate providing accurate and near-real-time data for the next phase. During this phase, we also establish data governance that focuses on the privacy and security of the data.

The second phase focuses on data analytics – predictive, prescriptive or diagnostic analytics that can help the various departments of your business with actionable insights. In this phase, we also help with rapid prototyping and experimenting with advanced analytics such as machine learning and AI. We help you adopt machine learning into your data analytics to help with your product innovation and offering you a competitive edge in the marketplace.

Our data management strategy provides an enterprise with quick and complete access to the data and the analytics it needs through four steps.

Our four-step solution for Enterprise Data Management is elaborated below.

  1. Collect: Ingestion/Data Prep/Data Quality/Transformation

In this step, we access and analyze both real-time and stationary data to reliably determine data quality and extract, transform, and blend data from multiple sources.  We then map and prepare the data to load into a target Data Lake. It is important to identify all your data sources and data streams to determine your data acquisition and establish the frequency of your batch process. This also involves establishing your infrastructure to help with the high volume of data streams and supporting a distributed environment.

Since multiple systems exist in silos, to make data-driven decisions with all members of the organization not operating off of the same data, Businesses these days are moving towards a single source of truth model to overcome this challenge.

With a single source of truth (SSOT), data is aggregated from many systems within an organization to a single location. This ensures zero duplication and hence, enhances the data quality. A SSOT is not a system, tool, or strategy, but rather a state of being for a company’s data in that it can all be found via a single reference point.

  1. Store

We use a scalable, reliable (a Cloud-Based Data Lake) comprised of various data repositories for both structured and unstructured formats to ensure reliable data storage. In this step, you cleanse, categorize, and store the data as per your business functions. For example, you can establish separate functional areas for sales, marketing, finance, and procurement-related data. This will help you establish a functional unit while identifying the need for data integrators across functions.

  1. Process/Analyze

Once the data is identified, organized, and stored, your data is ready for data analysis, building machine learning models, or statistical analysis. Data analysts or data scientists can run multiple queries or develop algorithms to analyze trends, discover business intelligence, and present outcomes to make smart decisions.

  1. Visualize

The output of the data analysis needs to be presented in a visual dashboard to provide meaningful answers to key questions driving business decisions.  Here, we not only provide insightful visual dashboards but provide search-driven “Google-like” products with Natural language processing capabilities to provide answers to easy-to-understand presentations for all levels of data users and the public. With products like Thought Spot, users can type a simple Google-like search in natural language to instantly analyze billions of rows of data.  Users can also easily converse with data using casual, everyday language and get precise answers to questions instantly.


Getting your data strategy in place is the first step to start with data analytics, data science and the AI journey. As the marketplace continues to rattle business models, adopting newer data analytics tools such as machine learning can help you not only stay ahead of competition but also continue to operate your business successfully in uncertain times. This can lead to a data-driven value cycle that can help pave the way for accomplishing the transformational change that is essential to become an AI-enabled organization.

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