Let’s see <strong>How Data Science Tools Can Link Information from Different Sources.</strong>
Let’s see How Data Science Tools Can Link Information from Different Sources.
The information gathered with the help of data science tools should bring some value otherwise there is no point to collect data. Most companies use data science tools to improve customer satisfaction, reduce expenses, and increase their profitability.
Very often people confuse the meanings of data science, data analytics, and big data thinking that all the terms have the same meaning. However, there is some difference between them. Data analytics helps organizations understand how their data looks like. Analytics is all about the investigation. Unlike data science, analytics has nothing to do with problem-solving.
Data science is closely connected with big data. However, the work of data science teams is not limited to the large scopes of data. They work with all types of information, whether it is structured or unstructured data.
The value of data science to business depends on the industry it serves to. For example, it can help grocery stores define what products to sell, or what products could be more popular based on different attributes. The most effective work of data science experts can be observed when their efforts are combined with those of business executives. The results of the teamwork often surprise the stakeholders as they detect the value in where they didn’t even expect it to be.
Usually, data science teams consist not only of data science experts but also of data analytics and engineers. Before, transforming data into business value with the help of data scientists and engineers, it should be first analyzed by analytics. Therefore, data science teams are put together of different specialists.
The main objective of data science tools is to extract the valuable information that is focused on business development. Data science teams must be able to define the business opportunities based on the insights they get from data.
The data science stack is huge and we can list for a long time. The key thing about the tools is to make sure that they meet the needs of the different stakeholders that are part of a data science team. In terms of the IT enterprise, you need tools that bring security and governance. As you evaluate a stack, a key thing is to make sure that the stack enables that within an organization.
For data scientists and data engineers, it’s a flexibility and making sure that they can use the best tools so that they can be as accurate and fast as it possibly can be.
For the business stakeholders, what they want is to drive value. So, you need tools that make it easy for you to deploy work quickly, and communicate the work more widely. It can be tools that enable you to create data pipelines, tools that enable you to create dashboards, and visualization. There are a lot of different ways how value can come out of data science.
SQL, Python, R, and Java are just a few tools used by data science teams to process information. Scientists also use a bunch of open source software like Oozie, TensorFlow, and Hive. These tools are used to extract, clean, analyze data, and perform other data-related tasks. Data science work is done with the combination of programming, visualization, and other helpful tools. Let’s take a closer look at some data science tools:
Hive is a system of database management based on Hadoop platform. It allows executing inquiries, aggregating, and analyzing data stored in Hadoop. Here are some opportunities provided by Hive:
Oozie is a workflow planning system operating on a server to manage Hadoop tasks.
TensorFlow is an open source library for machine learning developed by Google to solve the problems of neuron networks building and training to automatically detect and classify images.
If you are interested in a detailed information about other data science tools and their features, read a comprehensive article about top tools for data scientists.
According to IBM, data integration is a set of business and technical processes aimed at connecting data from different sources. As a result of this processes, data science experts get valuable information in a unified manner.
Consolidation is the first data integration type we will consider. Consolidation is a physical process of combining data from disparate places in a single unified place. The main objective of this process is to reduce the number of places where data is stored. ETL (extract, transform, and load) tools play an important role in the consolidation process. These tools extract data from various sources, make it clear, and put it into one data storage location.
Propagation copies data from one source to another.
Virtualization helps to see the real-time data from different sources in one place. This type of data integration does not require a unified format of data. It just retrieves the data.
Federation is a form of virtualization which uses a virtual database and builds a common model for heterogeneous data from multiple systems. Information is brought together and viewable from a single location.
Warehousing is actually data integration as it is all about data cleansing, formatting, and storing data.
Digital transformation is about how we exploit all sources of data to create and enhance products and services, enable new and efficient operations and processes, create new channels and business models. Digital transformation leads to a reduction in costs, the increase in revenue, the creation of new markets. These are great results for every large enterprise. Companies must enable digital transformation in order to stay competitive. If you are not leveraging your data, if you are not using data science work, you are going to lose your competition.
Data science is important for achieving digital transformation. Data scientists can be considered “the engineers” of digital transformation through machine learning, deep learning, and artificial intelligence. Companies are now going through a massive wave of change. Digital transformation of a company can consist of five key components:
When you embark on any digital transformation project, you should look at these key principles as your tests for how you truly transform the project or process that you are working on. Digital transformation means how the data scientists figure out what is the signal of their problem, what is the data we have available that we can leverage against that.
Digital transformation is ensured by three essential things such as follows:
So, to enable digital transformation you will need the right people, technology, and processes.
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When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them