Top 10 digital transformation trends for 2020 | Comidor Digital Automation Platform

Top 10 Digital Transformation Trends for 2020

Top 10 Digital Transformation Trends for 2020 789 526 Comidor BPM Platform

In 2020, the next stage of digital transformation welcomes us with fresh digital transformation trends that will unpredictably disrupt organizations.

Whatever a company’s vision for the future is, it must include a digital transformation plan in order to grow. Total investment between 2019 and 2023 in Digital Transformation technologies will increase between 15% and 20% across all sectors, according to the International Data Corporation (IDC).

In this article, we explore the 10 digital transformation trends that we believe will be the most significant in 2020.

Top 10 digital transformation trends for 2020

1. Low-Code Development

Organizations need a faster and simpler way to create applications—and low-code development platforms such as Comidor, are the ideal tools for accelerating app delivery.

Forrester Research defines a low-code development platform as platforms that enable rapid application delivery with a minimum of hand-coding, and quick setup and deployment.

Ιn fact, low-code enables business users with zero programming experience to create applications using drag-and-drop components through a graphic user interface. Thus, that means they can orchestrate how a business app works and create an experience tailored to them.

2. Support Vector Machines

Support Vector Machines (SVM) which constitute one of the future digital transformation trends are modern, effective supervised machine learning algorithms that can be used for classification or regression problems. Simply put, they do some extremely complex data transformations, then figure out how to separate your data based on the labels or outputs you’ve defined.

Some common applications of SVM are:

  • Face Detection
  • Text and hypertext categorization
  • Classification of images
  • Handwriting recognition

Beyond the above practical implementations, enterprises can benefit from SVM and enhance their decision-making processes. For example, organizations can use Support Vector Machines to conduct competitor analysis determining which competitors are comprising and predict their future.

3. Sentiment Analysis

Sentiment analysis has been a trending research topic over the last few years.

 But what’s so great about sentiment analysis? The answer is that it can turn customers’ feedback into meaningful insights.

Simply, these algorithms are smart enough to understand the tone of a statement, whether it has a positive or negative connotation. This can be applied by companies seeking to optimize their customer support, improve customer engagement, and plan product improvements.

4. Real-time Analytics

Analytics is one of the technologies that will dominate 2020.

More and more businesses are realizing that the future is in real-time data and it’s the best way to deeply understand customers. By combining the large volume of real-time data with new generation business intelligence tools, organizations can make more conscious decisions that will lead to growth, an increase in profits, and customer satisfaction.

5. Blockchain

Blockchain is a decentralized peer-to-peer network that is used to record transactions. The main benefits are decentralization, transparency, and immutability.

Gartner listed blockchain as one of the top ten strategic technologies for 2020.

According to the research, blockchain has the potential to disrupt industries by providing trust, and transparency and enabling value exchange across businesses. Also, blockchain can reduce transaction costs and improve cash flow. Moreover, Gartner predicts blockchain will be fully scalable by 2023.

6. Predictive Analytics

Predictive Analytics will be one of the hottest topics in analytics in the New Year.

According to Wikipedia, it encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about the future or otherwise unknown events.

Predictive analytics is no longer only for mathematicians and statisticians. More and more organizations are turning to predictive analytics to explore new opportunities and gain a competitive advantage.

What are its common use cases?

  • Prevent cyber attacks
  • Gain profitable customers
  • Establish payment policies

7. Master Data Management

At a time when data and information are one of the top assets for businesses, data governance is about to fill the gap of quality assurance and the right data identification.

Businesses are finding that it’s not easy to handle the exploding volume of data coming from diverse sources. The solution to this problem seems to be Master Data Management tools.

Master Data Management software manages businesses’ master data for many business functions such as customers, suppliers, locations, products, services. This improves the efficiency of applications and business processes.

By utilizing such software it’s much easier to perform deeper analysis, build new relationships with customers, and drive decision-making insight for faster growth.

8. From DSS to IDSS

A decision support system (DSS) is an information system that supports business or organizational decision-making activities. Intelligent Decision Support Systems (IDSS) are coming to enhance decision making by using data, and expert knowledge to solve semi-structured problems by incorporating artificial intelligence techniques.

So, what’s the difference between DSS and IDSS?

A traditional DSS is a decision tree-driven solution, built by humans and is automated.

IDSS offers insights and proposed actions to decision-makers based on the problem, any previous actions that were taken, and the results of those actions.

Are DSS systems safe?

Security is crucial for decision support systems. Security professionals guide DSS users on how to secure their passwords, equipment, and data in order to prevent the loss of customer and decision support data.

9. Natural Language Processing (NLP)

Natural language processing (NLP) is about developing applications and services that are able to understand human languages. Some practical examples of NLP are speech recognition for e.g. Google voice search, Google autocomplete, and language translation.

NLP provides businesses with numerous advantages and can be applied by many departments. For example:

  • In the HR department: recruiters can analyze the CVs and create summaries of them. Also, they can predict how well a candidate will fit into the company based on additional interviews or other data.
  • In Advertising: By analyzing all the digital channels, NLP helps advertisers identify new audiences that may be interested in their products. This helps marketers broaden the range of channels for ad placement. In this way, they are able to spend their ad budgets more effectively and targeting potential clients.

10. Conversational AΙ

Conversational AI is the use of messaging apps, speech-based assistants, and chatbots to automate communication and create personalized customer experiences.

With these tools, businesses have the ability to carry out highly personalized interactions with a large number of individual customers.

Two exciting Conversational AI trends for the new year 2020 are those below:

  • Self-learning Conversational AI:

Unlike decision-tree bots, self-learning chatbots will enable businesses to train models with consumer data, product details, and digital footprints (social media, email campaigns, etc.)

  • Conversational AI Lead Generation:

Lead Generation is the core essence of any business strategy. However, it may be challenging and time-consuming. In 2020 conversational AI tools will have the ability to capture leads, follow up, respond to customers’ queries, and even schedule meetings automatically.

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