Apart from the better understanding on data, we need to pay more attention towards the basic statistics as it is the key concept of driving the data to develop interactive visualizations and convert tables into pictures.
The rapid rise of visualization tools such as Spotfire, Tableau, Qlikview and Zoomdata, has gained immense use of graphics in the media. These tools currently hold an ability to transform the data into meaningful information with proper standard level principles in the statistical visualization world. They are very helpful in translating the analysis into pixels when you are ready with the cleansed data/analysis.
It is quite often in everyday life, we get to see a massive amount of data crawling that is continuously increasing. Now the question is how to get better insights before making any decision for a business problem. Let us assume you are inclined towards a specific sector say sales (Or any other sector like finance, marketing & operations) that has billions of records and you want to draw a graphical layer on the data set for a better decision. Being a decision maker, you should probably think about a way to analyze the data, which is a data visualization Tool. Now the question arises on what data visualization is and why to adopt data visualization?
This blog post is about data visualization. It explains in detail on how to convert the massive volume databanks into statistical graphics in a pedagogically meaningful way.
Data Visualization in the Pharma Industry
Pharma industry faces unprecedented challenges that have an impact on development, production and marketing of the medical products.
It has been facing declined success rates in Research & development, patent expirations, global sales, medical bill review, reference based reimbursement system, drug testing/clinical trials, electronic trial master file, Hospital Food, drug and maintenance administration due to huge volume of databanks coupled with lack of decision making strategies where key element of cure is big data and the analytics that go with it. Big Data helps in organizing your data for future analysis and to derive new business logic from it. You can also change the current business logic as per the data trend to increase your business throughput.
Figure 1: Data Processing in the Pharma Sector
Key Challenges faced by the Pharma industry
- What is the Market distribution by segment?
- Why prescription medicines (Rx) and R&D spending are high over the year?
- What kind of generic manufacturers generate revenue?
- Which biological drugs will go generic in future?
- What is the value of expiring patents? Does it need to be renewed?
- How is the disease distribution substance raised over the years?
- Is the progress of clinical trials test results positive?
Data visualization and analytics are collaborating with both the internal and outside world as pharma sectors are turning up to be partnered with proprietary data visualization tools.
Importance of Data visualization
Importance of data visualization in any organization has framed a great impact on the astrological phenomenon in many industries. Successful businesses are built on informed decision making and this depends on the collected information in a graphical representation. Informed decision making is crucial for:
- Improving operational efficiency
- Detecting and responding to business change
- Identifying business opportunities
- Measuring and monitoring productivity
- Uncover inefficiencies in the supply chain
In general when any analyst tries to draw a conclusion on a raw data, it usually takes more effort literally thousands of hours, whereas Data visualization helps in automating many number of processes converting thousands of hours into seconds in a simple view of dashboards.
A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.
There are some practices or processes that need to be identified or addressed immediately and these can be done using the Key performance indicators (KPI). Using the data visualization tools, we can list out the areas has to be developed to highlight success rates. It is important to identify key areas before implementing the KPI’s in the dashboard views such as:
- Complete access/knowledge to organization strategies.
- Identify the impact of execution strategies on business drives.
- Differentiate long term & short term goals and prioritize the need.
Why do we need data visualization tools?
Key features common to all data visualization tools:
- Multi-source: All the visualization tools are now capable of connecting one or more data sources, which means there is no need to incline on one data source. It helps business to choose their desired data storage based on their availability. Data visualization reads and connects to multiple sources to bring meaningful insights.
- In Memory engine (Extract): It is a new functionality pitched into the visualization market, where we can extract the data set as cached memory into the server. It eliminates the “go get” from data source as the relevant data is loaded into the RAM memory all the time. Basically it increases the performance of the dashboard.
- Live connection: Data sources are generally occupied with huge amounts of data. Using an extract may slow down the server performance, so it keeps the underlying data in the database reflecting the live data.
- Data Blending: Data blending is a process of clubbing different sources that supplement different data tables. It sends the query to the respective database and the underlying results will be combined by the data visualization tool including the aggregated data in one workspace.
- Calculated variable/columns: This feature allows to perform complex calculations as custom expressions by using standard pre-defined functions and operators apart from the predefined columns of the data table. It performs dynamic operations on the existing underlying attributes used in custom expression and displays the required desired output value. In one way it is also called as a global variable and can be called across any N number of visualizations.
- Maps and Geocoding: A trend changing visualization which allows the users to grab a look on their business variations globally. Mapping functions come with different layers and can be configured separately with regards to coloring. It plots the data points based on the latitude and longitude values over the graph and you can plot pie charts, heat maps, tree maps and scattered plots.
- Filters: Filters are used to change the content of the data in current visualization, when applied it puts a restriction on the underlying visualization with some specific attribute value. There are different types of filters such as extract filters, context filters, quick filters, user filters, filtering scheme, etc. that acts with different behavior based upon the data visualization tool. These filters are simple to understand and any end user can easily make use of these filters.
- Visualizations: In simple terms these can be termed as graphics or a pictorial representation plotted on different axis to see the relationship in the data communicating insights in a powerful and meaning way.
- Scheduling automation: Automation is a process which is defined at server level for the users who opt to view the dashboards in their inbox or some shared location without accessing the respective data visualization web browser. This process is ideally helpful for the senior level managers who wish to make spot business decisions. The automated reports can be exported to PDF, HTML, Power point and excel format depending upon the tools capabilities and user requirements.
Stay tuned for Part2 of this 2 part series on Data Visualization to know more about the unique features of each tool and their role play. Please send us a note with your queries and feedback.
About the Author:
Rajsekhar Battula is a Technical Consultant at Mastech InfoTrellis and has expertise on Tibco Spotfire, Tableau & SAP BI. He has over 5.5 years of experience in Data Visualization.