Top 10 Data Analytics Trends to Look for in 2020

Original article can be found here (source): Artificial Intelligence on Medium

Data Analysis Skills Will Continue To Be In High Demand

Top 10 Data Analytics Trends to Look for in 2020

Here you can easily get a clear idea about where and how Data analysis helps to grow faster in the market

Overview :

Data and analytics enable all parts of the digital business. The amount of data created, including data collection, data analysis, and data formation. Data and analytics are continuously implemented as key players in companies to increase business efficiency over the years.

Data analysis trends over the years range from a departmental approach to a business-oriented data approach, increasing the focus on adopting agile technologies and increasing analysis.

This is because data and analytics play a larger role in the IT sector, according to Gartner analyst and vice president Rita Sallam. Data and analytics have become important parts of the way it serves customers, hires people, streamlines supply chains, streamlines finances, and performs many other key functions in the organization.

Here’s the “Top 10 Data Analytics Trend” that the IT world will be talking about in 2020.

  • Data Quality Management
  • Augmented Analytics
  • Artificial Intelligence
  • Data-driven Culture
  • Data Automation
  • NLP (Natural language processing)
  • Graph Analytics
  • Data Fabric
  • Blockchain
  • Cloud Usage

1. Data Quality Management :

Data quality management (DQM) refers to a business principle that requires a combination of the right people, processes, and technologies to improve the most important data quality criteria for an organization.

Accurate and up-to-date data provides a clear picture of your company’s daily transactions, so you can count on yourself in the top and bottom applications that use all of this data.

A good data quality program uses a system with several features that help increase the reliability of your data. Data purging helps correct duplicate records, non-standard data displays, and unknown data types. Cleanup implements the data standardization rules required to obtain information from your data sets.

2. Augmented Analytics :

It plays an important role in empowering more people in an organization to gather information from data without the need for more analytics, complex mathematics, or computer science.

In 2020, it will be the primary acquisition of companies dealing with increased analytics, analytics, and business intelligence.

3. Artificial Intelligence :

Artificial intelligence models are gradually being used to increase and support human decisions. Augmented intelligence is one of the main characteristics of artificial intelligence that the analytical industry will adapt, and deep learning and natural language processing are under the same roof.

However, big data techniques use AI models only for specific tasks. For example, data analysts often use artificial intelligence for auxiliary roles. This type of use is often referred to as augmented intelligence to augment human intelligence rather than change artificial intelligence.

4. Data-driven Culture :

Data is very valuable when making decisions in businesses. Therefore, performing the data culture can be useful as employees have more power and need to make data more convenient within market segments.

Tips to help you build a data-driven culture

  • Set a clear vision and goals
  • Using data to make decisions
  • Connect data to business goals.
  • Integration is king
  • Make data accessible

5. Data Automation :

Data automation is the process in which you analyze the information you need as a business owner from all the data you collect and prepare for use. It provides automation opportunities to facilitate data collection, analysis, monitoring, and reporting.

With the help of data automation around, companies responsible for data analytics are focused on analyzing data science products, while making it simple for citizen data experts to use.

6. NLP (Natural language processing) :

NLP is not just limited to voice commands. It also includes chatbots, virtual assistants, or any human to machine interaction.

NLP and interactive analytics are highly complementary to augmented analytics. It provides a new type of interface for non-data experts, queries, and analytics.

It is analysis brings convenience to another level and makes a problem as simple as a digital search assistant or Google-like discussion (like Alexa). Any user can ask questions using text or voice, with increasingly complex questions and answers.

7. Graph Analytics :

A number of analytical techniques are called graph analysis, which shows how various entities, such as places and people, are related.

To take advantage of graphics technology to solve business challenges, you must understand the basic concepts on which graphical analytics are created. Chart analysis includes a number of analysis techniques that allow you to explore relationships between entities

8. Data Fabric :

Data Fabric is designed mainly to provide reusable data services, semantic layers, channels or APIs through a mixture of data integration.

A data structure is extremely important as it allows applications and different tools to access the data. it explains the transition to digital transformation by simplifying integrated data management methods with businesses, internal situations and in the cloud.

It provides a logical data storage architecture that provides seamless access to data and heterogeneous storage.

9. Blockchain :

We have talked about the basic concepts of Blockchain many times before on this site. Therefore, to give you a brief explanation, a blockchain is simply a series of time-stamped data records managed by a group of computers that do not belong to a single entity.

Blockchain technologies address two problems in data and analytics.

  • Relatives of assets and transactions.
  • Transparency

Each of these data blocks is pinned and linked to each other by encryption policies. Blockchain is transparent so you can monitor the data if you want

10. Cloud Usage :

The use of the public cloud continues to grow as more and more organizations request the service.

Utilizing a variety of optimal tools and solutions available in different clouds enables organizations to maximize their benefits. Despite the advantages, the use of multiple clouds can make cloud monitoring, governance and administration costs difficult.

Wrapping Up :

We are now sure In 2020, data and analytics will be part of the rapid growth and development of the IT sector. It is a great opportunity to deal with the best data analytics trends to keep your business at the lead of the game for a more competitive future.