Importance of Data Science for Businesses

by | Published on May 3, 2017 | Data Entry Services

Data is a crucial element for any business as it is necessary to make informed decisions. Organizing and controlling a large amount of data becomes easy with data entry services. Data is an organizational asset and analyzing the structured data and Big data will help to derive value to gain deep business insights. The raw information or data stored in enterprise data warehouses is to be cleansed and mined to build advanced capabilities.

Data Science – How Important Is It for Businesses?

Data science is an important term in this regard. Also known as data driven science, it is defined as an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms – structured or unstructured. Data science combines the technology of data analysis, visualization, statistics, mathematics and the knowledge of business prerogatives. Statistics play a significant role in fitting patterns to data sets. Descriptive statistics helps in categorizing and describes what is shown by the available value whereas inferential statistics helps in deducing possibilities beyond the available data. Data science will help understand hidden patterns from the large volume of data, which can then be utilized commercially. Data science is different from standard business intelligence in that you are unsure about what you are looking for. The attempt here is to identify hidden patterns of commercial value. It involves the following steps:

  • Comprehensive, quantitative data analysis to discover data insight
  • Developing data products
  • Deriving business value

Turning data into product is not an easy task. Data product development comprises:

  • Defining a problem
  • Hypothesizing the desired outcome
  • Identifying the data needed for analysis, and ensuring its cleanliness, completeness and reliability preferably with the support of data cleansing services
  • Analyzing the data from different viewpoints using domain knowledge, visualization techniques and statistics to reveal trends or patterns
  • Designing experiments to ensure the accuracy and repeatability of a specific pattern in diverse scenarios
  • Represent the successful pattern as an algorithm, build models that a machine can learn and use for analysis.

Some examples of data products are:

  • Recommendation engines such as those used by Amazon (for various items), Spotify (for music) and Netflix (for movies) that make suggestions to buyers.
  • Computer vision in self-driving cars wherein machine learning algorithms are able to recognize other cars on the road, pedestrians and traffic lights among other things.
  • Spam filters in mail applications that have algorithms that process incoming mail and determine whether an incoming message is junk or not.

What to Consider for Creating a Data Product

Data quality: To successfully create a data product, it is important to have good quality data. This ensures that the patterns being aligned to the data are not made vague by wrong or irrelevant data. Clean and relevant data helps shorten the time required to identify the pattern and increases the success of the data product.

Feasible business model: Data products are usually combined with other offerings that generate revenue. It is essential to assess the additional value the data product will bring to such products and also whether the effort taken to create the data product is justifiable.

Data science enables data conversion of data assets into data products. The IDC (International Data Corporation) predicts a need for 181,000 data scientists by 2018 in the US and requirements for five times that number of positions with data management and interpretation capabilities. The McKinsey Global Institute estimates the shortage of data scientists in 2018 at 190,000. This highlights the importance of data science in the contemporary business world.

Data science can be used in various important sectors. It is used to expose frauds and test risk models to evaluate credit risks. Businesses need to be more data driven to grow and improve. Organizations can merit from the guidance provided by data insight, as well as from the practical uses of the data products developed. Data-driven companies are those that focus on improving operational and management processes by using data analysis, and also on developing data products that are delivered as a service to customers.

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