Continuing on the Research of “Top 25 Digital Startup ideas and technologies for 2017” In this section, we evaluate and highlight aspects of “Trend #4 : Big Data – Data aggregation and visualization tools”
What does Data Aggregation mean? According to techopedia, data aggregation is a type of data and information mining process where data is searched, gathered and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis.
Data aggregation is a fundamental practice in data collection and analytics. The premise is that data aggregation, when done right, will help address challenge of ever increasing demands of big data. It is the process in which information is gathered and expressed after data is ‘mined’. Data aggregation allows consumers of business intelligence products to quickly assess, draw conclusions and make decisions based on large amounts of raw data.
Why should corporate executives pay attention to Data aggregation?
Implementing data aggregation solutions can generate significant performance benefits, opening up opportunities for companies to enhance their organizations analysis and reporting capabilities.
Enterprises need to integrate and aggregate data from disparate sources including:
- Corporate data. Information on sales and marketing, pests and other inputs are constantly analyzed by organizations involved in the production and supply and marketing.
- Customer data. Individual customers and clients generate a lot of data from their operations and interactions with corporate clients.
- Public data. Public data (including data from national, state, and local government agencies) and other information may either be available freely or sourced from data aggregators.
- Emerging data. Adoption of consumer-concentric uses of the IoT continue to await mass adoption, but consumers are already beginning to leverage data from various sources.
Integrating data from across traditional and emerging data sources requires an understanding of data formats, types, frequency, aggregation, and translation of such data. Rules and regulations with respect to data ownership and stewardship — especially of farm-specific data — vary across countries, states, and provinces. Therefore, agronomy data scientists also need to be cognizant of regulatory challenges guiding storing, aggregating, and retrieving such data.
Humans need the ability to glean insights from large volumes of structured and unstructured data. Visualization of data enables analysts and decision makers to “see” the aggregated data visually, so they can grasp difficult concepts or identify new patterns.
Visualization tools using technology to drill down into charts and graphs for more detail can help users interactively engage with data to make sense of patterns that may or may-not be apparent. The tools may enable users to interactively change what data they see, and how it’s processed.
Why should corporate executives pay attention to Data Visualization?
There is a confluence of forces impacting the way consumers interact with information technology, including what some in the industry collectively call SMAC — social, mobile, analytics, and cloud — that present unique opportunities.
Innovative data aggregators, organizations, and scientists are applying different types of analytic techniques like investigative data discovery, descriptive data aggregation, predictive analytics focused on outcomes, and other prescriptive techniques. In Figure below, we see a framework for analytics in corporate environment:
At the core of the framework are structured and unstructured data sources. Organizations, government agencies, and other research organizations generate reports and transactional data in formats that can be stored and retrieved from relational, structured databases. Such transactional and reference data may exist in databases within software applications running commercially developed databases like IBM’s DB2, Microsoft’ SQL Servers, or Oracle. Such data can be cataloged, indexed, and queried using well-understood tools and techniques.
Social media, satellites, drones, and sensors also generate vast amounts of unstructured and big data that may include images, text, and other data structures. Emerging big data analytic techniques are being applied to make sense of data. Instead of such empirical analysis that takes time, users are also embracing results from analysis of large, real-world data sets from public sources; and analysis of such big-data can produce reliable recommendations much more quickly.
Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner.
Data visualization can also:
- Identify areas that need attention or improvement.
- Clarify which factors influence customer behavior.
- Help you understand which products to place where.
- Predict sales volumes
A few popular Data Visualization tools include
|Google Chart||Google is an obvious benchmark and well known for the user-friendliness offered by its products and Google chart is not an exception. It is one of the easiest tools for visualizing huge data sets. Google chart holds a wide range of chart gallery, from a simple line graph to complex hierarchical tree-like structure and you can use any of them that fits your requirement.|
Opportunity for Startups in Data Aggregation and Visualization Tools
Corporate digitization efforts and the need for expertise to guide the transformation translates to opportunity for consultants and software product development firms. Startups have begun exploring new and innovative techniques for data aggregation and visualization.
Analyst firms periodically review Systems Integrators in this space. A few articles and research reports on the topic:
- Visualization is the future: 6 startups re-imagining how we consume data – Interesting article from 2013. Many of these startups have since been acquired.
Compiled and Edited by: Mohan K | Research assisted by Sanwar Tagra, who is currently pursuing his Bachelors in Engineering at BML Munjal University. |
| Reproduction with permission only |