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The Importance of a Strong Data Foundation

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by Harika Panuganty | Last Updated: November 18, 2020 | 1 min read

Good data is the foundation for any digital transformation initiative, and its strength comes from the accuracy, comprehensiveness and periodicity of data management. Without the right tools to help you manage and process data; however, the best data in the world can be meaningless. That is why data management tools are so important.

Data management is the process of data collection, organization, analysis and visualization, and following is a step-by-step explanation of each of these topics.

Data Collection

The quality of the data determines the veracity of the analysis. In a corporate setting, data is often gathered from both internal and external sources with various frequencies. The frequency of incoming data depends upon the data refresh rate, which can be updated through user action, system event or time function.

Quality data contains all attributes and is collected on every update event – through internal systems, IOT devices, scraping tools, synthetic data generators, public data sources or data augmentation tools. Generally, incoming data must be accurate, comprehensive and of high caliber to be able to integrate easily with data analysis tools and techniques.

Data Organization

Once the data is collected, it’s important to keep it organized because it contains valuable insights that will lead to better business intelligence and data-driven decisions. The data structure depends not only upon the data elements and their attributes, but also on the analysis that needs to be performed on the data. This structure can be time series, relational or unstructured. Data is stored in open source systems such as Apache Cassandra, Couchbase, DynamoDB or commercial environments including Microsoft SQL Server and MongoDB.

Data Analysis

The next step in the data management process is to identify patterns in the data by using machine learning and predictive analytics. Machine learning algorithms analyze data, learn from the data and then proceed to make unique recommendations for next best actions and decisions.

Gartner predicts that by 2022, 75% of new end-user solutions leveraging artificial intelligence and machine learning techniques will be built with commercial solutions instead of open source platforms. Unlike open source software that is available to the public, commercial software has source code that can only be edited by the person, team or organization that created it. Several data analysis platforms exist in this area, including MATLAB, Google Cloud AI, Microsoft Azure Studio and RStudio.

Data Analysis discussion

Data Visualization

The output of data analysis can be represented in various formats – through charts, tables, slide decks and dashboards. Several visualization platforms are also available, such as Tableau, Qlik Sense and Power BI.

Microsoft PowerBI is the industry leader in the interactive visualization and business intelligence space, enabling end users to easily create their own reports and dashboards. The platform also offers data preparation, data discovery, interactive dashboards and augmented analytics – the complete package for effective business intelligence and analysis.

Now that the foundational elements of good data management have been explained, it will be easier to understand the brilliance behind P360’s BirdzAI platform for pharmaceutical commercial organizations.

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P360 Merges Good Data Management into One Powerful Solution

All of the data management considerations above are important when making business decisions. Without strong foundational data that is easy to use, one may not be able to make necessary business decisions, or even build upon that foundational data.

P360 offers a comprehensive suite of analytics tools for companies within the pharmaceutical industry. Data360, P360’s pharmaceutical analytics and data management solution, is tailored to meet your company’s business intelligence needs by supplementing existing business processes and operational flows. Built on Microsoft Azure Cloud and PowerBI, Data360 is the only platform that provides an end-to-end data analytics and management solutions for the pharmaceutical industry.

Data360 is the smart analytics and data management platform with a desirable set of tools to support your organization. The platform uniquely leverages predictive modeling and machine learning to share findings, make predictions and drive better decisions using built-in analytical models. These models generate results far more accurately and precisely than humans, as they are able to utilize a multitude of variables and data points. Additionally, Data360 accomplishes the following:

  • Combines data from multiple sources into one configuration

Companies operate with a large amount of data coming in from varied sources – both internal and external. Internal sources can include customer profiles, sector budgets and production reports, whereas external data sources might come from industry groups, vendors, regulatory agencies, etc.

The difficult thing about collecting data from various sources is that each data source has its own structure and formatting. However, integrated platform like Data360 are able to efficiently format all of the data as a single unit. This helps create constancy across the enterprise, and promotes efficient collaboration and data share between employees and groups.

Data360 utilizes Microsoft PowerBI to create a powerful and uniform environment, making your work much easier. The platform also has the capability to scale up on users, complex analysis and workload as your data needs grow.

  • Derives insight from raw data for better data-driven decision making

The overarching goal with data is to be able to gather insight from the dataset that can be used for effective decision-making. In the pharmaceutical industry, it’s quite common to be working with various providers and vendors. With multiple data sources, gathering and analyzing data can become quite tedious.

The most time-consuming portion of the process is wrangling the data from the different sources – internal and external – into a consistent and compatible format. Building infrastructure using an integrated platform helps sort, standardize and route the data to the appropriate locations in an organization’s system. Having an integrated platform such as Data360 makes the process simple, since it incorporates pre-built integrations with most of the industry vendors for all major commercial functions.

data on computer screen
  • Provides uniformity across the enterprise between employees, leadership and stakeholders

In addition to saving time, having a smart solution to do the hard data-related work helps standardize your data across the enterprise. If individual employees search, collect and analyze data in silos, there is a strong possibility that there would be inefficient redundancies in data processes. Data360 ensures that every employee in the organization is operating on the same page by providing access to dashboards, data resources and data streams. Utilizing this integrated data foundation, an organization can be sure that the data they are receiving is complete, consistent and correct.

The Data360 platform powers organizations through all four stages of good data management, from collection to visualization. With P360, your organization will be able to efficiently organize incoming data, and process it using advanced machine learning models that help generate key insight. These insights are then displayed through pre-built reports and dashboards, helping your leadership team make effective data-driven decisions.

Data360 also acts as the backbone for other software solutions, integrating seamlessly with them. Without strong data, other solutions can’t succeed.

About P360

Based in Piscataway Township, New Jersey, P360 is a leading developer of technology for the life sciences industry. Product offerings include BirdzAI, PatientJourney360, Data360, Trials360 and Swittons. To learn more about P360, visit P360.com.

Author Bio: Harika is an aspiring data professional and holds a master’s degree in biomedical informatics with a focus in data science. She’s passionate about all things data but particularly, machine learning and artificial intelligence. Outside the data world, she’s a content writer in the areas of science, education and health. Feel free to connect at https://www.linkedin.com/in/harikapanuganty/