Without Data Science experts, a cutting-edge technology, and an effective data-driven culture, an organization cannot derive actionable insights from their awash data. It is making a profound impact on business accomplishments. A data-driven company is the one where data is the basic requirement which finally becomes the value generator.
These days, organizations are becoming more agile and insights-driven. For every business, Data Science is the pillar, since Data Analytics and Machine Learning are deriving strategic business decisions, operational efficiency, and improved customer satisfaction. As per Forrester’s report, data-driven companies that harness insights throughout their workflow and work on it to create competitive advantage are growing at a rate of 30 percent annually and are aiming to make $1.8 trillion by the year 2021. Hence, it is very essential for a data-driven company to streamline their Data Science workflow to mine data from their repository and fetch valuable insights from that. It is also equally important to have professionals who have completed their Data Science Course from an industry recognized training platform.
What Does a Successful Data-driven Organization Look Like?
There is no ideal path to become a data-driven company; however, it can be measured with certain characteristics.
Technological and Organizational Characteristics
The technology part comprises integrated data management and support system, good quality of data, strategic tools for analytics, automation, a data-oriented programming language such as Python, etc. Python is an integrated programming language that is specifically built for data structures. Python course will yield you expertise in Data Science while you are pursuing your Data Science course.
The organizational part comprises robust leadership, governance, empowered team, proper project management, etc. A successful data-driven organization is led by creative and passionate executives who are open-minded. As we all know, a combination of the right people, the right technology, and the right process is the fundamental requirement for any successful operation.
In this digital age, the success of an enterprise is reliant on its staff’s data literacy. Data literacy means the capacity to read, analyze, work, argue, and play with data. In order to overcome the issue of data illiteracy, Airbnb started its own Data University where data education was provided to everyone across the company. Be it a manager, an engineer, or a designer, regardless of the job role, every employee received an opportunity to learn how to use, analyze, and visualize data across Airbnb’s toolset. They also learned how to make insight-oriented decisions. The Data University platform was successful which helped in transforming the data culture at Airbnb.
Yet another significant aspect of being data-driven is having a well-structured automated suite of tools. Organizations should take necessary steps to automate the refined data and leverage these insights to business processes. However, it is difficult to enhance and accelerate data-driven business processes, if the data workloads are not broadly automated. If the data flow and pipeline are automated, the time taken for the entire process is reduced; it will enhance decision-making, execute transactions, rethink strategies, etc.
Data-driven Culture Across the Enterprise
Culture in an enterprise plays a crucial role. More than technology and strategies data-driven culture matters. Data-driven culture means empowering the employees with transparent access to data and make everyone understand how this data impacts business operations.
Challenges in Becoming Data-driven
Top industry players like Amazon, Microsoft, Google, etc. have set the business models based on connecting cognitive analytics. They combine Machine Learning and Artificial Intelligence to mimic the way decision-making is done by humans. Three challenges faced by an organization in becoming data-driven are explained below:
Data is not centralized: If the data across the company is not centralized, it means that it is not reachable to the right channels. Data is residing on people’s computers, files, spreadsheets, etc.
Bad quality of data: Many companies have this issue where data is not in the format or quality which is required for analytics purposes. The data capturing tools are not quite erudite to pick just the rich sets of attributes that are suitable for the Machine Learning Algorithm.
Dark data: The data which is invisible such as the one that is residing in emails, shared files, communication systems, etc. are known as dark data. Since the value of such data is not determined, it is termed as dark data.
A Journey from Data to Decisions
Using data to derive value and control decisions is not a new perception, but the effective use of data with analytics to enhance the organization’s value is a distinguishing feature. The journey from raw data to the business driving decision is a designed approach that comprises data creation, data acquisition, information processing, and running the business process. Below are the potential areas where this designed approach can be applied:
Customer acquisition, retention strategies, satisfaction strategies, and profitability
Operations and performance management
Supply chain and delivery channel strategy
How Data Science Is the Pillar of a Data-driven Company?
Data Science and Machine Learning are rapidly becoming critical for businesses to survive. Let’s categorize the impact of Data Science and Machine Learning into four different potential areas and understand how they help in deriving better results.
Data Science has the key to divulge effective solutions for old problems. Data Scientists come up with new approaches to solve these problems.
Data Scientists’ job sometimes is based on a trial-and-error method. They need to apply ‘Big Data expeditions’ in some places where there is no clear vision and objective. They must explore the data for never discovered value.
Data Science can provide radical new solutions where human brains fail. It beats humans by resolving complex and data-rich problems with utmost ease and serenity.
Data Science is very commonly applied to continuously enhance the process and products. In a product-based company, the Data Science framework will have a model for continuously refining processes and products as per the data type their company collects. The examples of such companies are marketing groups, banks, retailers, etc.
Data Science is helpful to any industry in any way; it can add value to simple processes like hiring great people, making better and informed decisions, and identifying the attrition rate within a company or the reason behind the attrition. However, organizations can make the most out of this concept only if they have the realm to use it in the right direction.
The data-driven organizations can succeed with the right set of skillful people. Today, there is a huge need for certified Data Scientists. They are among the highly-paid professionals across the IT industry. As quoted by Forbes, the best job in America is to be a Data Scientist who draws a base salary of $110,000 per annum. Beholding the enormous requirement which is growing frequently, McKinsey predicted that there will be a 50 percent gap in the supply of Data Scientists against its demand in the future. If you are looking forward to having a career in Data Science, here is a fabulous Data Science Training Course offered by Intellipaat.
Sonal Maheshwari has 6 years of corporate experience in various technology platforms such as Big Data, Data Science, Salesforce, Digital Marketing, CRM, SQL, JAVA, Oracle, etc. She has worked for MNCs like Wenger & Watson Inc, CMC LIMITED, EXL Services Ltd., and Cognizant. She is a technology nerd and loves contributing to various open platforms through blogging. She is currently in association with a leading professional training provider, Intellipaat Software Solutions and strives to provide knowledge to aspirants and professionals through personal blogs, research, and innovative ideas.
Published by Lavismichel Inkel