New age enterprises need new Oil – Data. How can Saivi help in your end to end data strategy?
Data has been described as the new oil of the digital economy. Data are to the 20th century what oil was to the 19th century – the key factor for growth and change. Data is emerging as a new asset in today’s’ world. To survive the digital revolution and to be in a competitive market, data has become highly important. Flows of data have created new economics. Analyzing the real-time data flows which are mostly considered as the unstructured data is the new economy. Global data flows have increased global GDP by at least 3.5 percent – McKinsey.
Data are measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images, or other analysis tools. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Data can be turned into any number of artificial intelligence or cognitive services, and considered as the new sources of revenue generation. Proper Data utilization leads to creating a competitive business model possible.
In the 21st century, data is directly proportional to innovation and growth and it is considered to be a core asset in today’s digital economy. Data pave way for new industries and ecosystems creating significant competitive advantages. Organizations across the world are starting to leverage data and analytics to identify their business potential, growth opportunities, profitability, operational excellence, and cost reduction.
A report from Harvard Business Review shows that 55% of organizations agreed that data analytics for decision-making is extremely important today, and 92% confirmed the increasing importance of data and analytics through 2020 and 2021.
Data is an asset for any organization looking for a smooth digital transformation. Digitalization without data is meaningless. To moving towards a data-driven digital transformation, firstly, an organization should focus on a data strategy. Data Strategy ensures a sustainable competitive advantage in the future and with a great rate of return on investment. Organizations across the globe are focusing on data strategy to optimize their technology investments and lower their costs.
Data Strategy is about doing the right things to process data into insights for the organization. Organizations adopt a data strategy to create a vision for collecting, storing, managing, and using data in the best possible way. Data strategy involves four steps. They are,
- Data Sourcing
- Data Labelling
- Adding Cognitive Intelligence
- Data Visualizing
Data Sourcing:
Determining the source for obtaining data is one of the trickiest parts. Data sourcing is often overlooked and often not considered seriously. Data sourcing is the core activity of data strategy and without data flowing in, the organization will not be able to perform and progress. Data sourcing has multiple approaches such as data scraping, data extraction, and data aggregation.
(a) Data Scraping – Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program. Data scraping is the process of extracting valuable data from a website. The data scraping process is not only used to extract data from the web, in many cases it is used to channel that data to another website.
(b) Data Extraction – Data extraction is the act or process of retrieving data out of data sources for further data processing or data storage. Data extraction uses tools to scrape through online resources to collect valuable and needed information. Organizations extract data, process it further to analyzing it, and understanding the pattern.
(c) Data Aggregation – Data aggregation is the compiling of information from databases with the intent to prepare combined datasets for data processing. Organizations use data aggregation to process that data together. Data from multiple sources are extracted and combined into one place by using tools. This process is done to derive new insights and discover new patterns.
Data Labeling:
Data labeling is the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. Data annotation is used to categorize and label data for AI applications. Any training data for a specific use case should be properly trained and annotated. Data annotation uses the text, images, and videos to annotate or label the content. It improves output accuracy. According to the 2020 State of AI and Machine Learning report, 70% of companies rely on text. Data annotations for text include a wide range of annotations like sentiment, intent, and query.
Adding Cognitive Intelligence:
Cognitive Intelligence is an important aspect of Artificial Intelligence. Cognitive intelligence provides more of an analytical approach in decision making. Text and Vision analytics plays a major role in the success of CI. Text analytics is an AI service that uncovers insights such as sentiment, entities, and key phrases in unstructured text. It uses natural language processing (NLP). Text Analytics can classify a broad range of entities in text, such as people, places, organizations, date/time, and percentages. It can quickly evaluate and identify the main points in unstructured text. Vision Analytics on the other hand uses deep learning techniques on images & live video streams collected from surveillance cameras and mobile phones. Data obtained from multiple sources in the form of images and videos, will be analyzed through VA and react suitably.
Data Visualizing:
Using data and converting it into a visual context such as a map or graph is Data visualization. In general, the human brain understands the visual format much easier and simpler and DV exactly provides the same. This makes it easier for human brains to identify trends, patterns, and outliers within large data sets. Data Visualization identifies and clarifies the factors influencing the behavior of a customer. There are multiple DV tools available such as Google Charts, ChartBlocks, Hubspot, Tableau, etc.
Organizations adopting emerging technologies such as AI, ML, Big data, Cloud Computing, etc. use data for improving decision making. Not only decision making, data rightly used can help organizations improve operational efficiency, access real-time information, enhance customer experience, and have an edge in the competitive market.
If your organization is looking for a partner to support, suggest, and develop an end to end data strategy, OptiSol can be the best fit. We help organizations use practical data analysis to solve everyday business problems. To discover how OptiSol Business Solutions can help your organization in implementing the right end to end data strategy, you can visit https://saivi.optisolbusiness.com/. Savi is our comprehensive offering that covers end to end data-related services and our expert consultant can help you all the way from sourcing to visualization. Data Journey, as discussed in the article is of 4 stages. We offer custom solutions in each of these phases that will accelerate your digital journey and realize the power of new oil.
