The technological landscape is an ever evolving one. New trends, software and applications appear regularly and are continuously expanding the role that data plays in decision making and operations for business.
What are the emerging analytical, big data and data management trends for 2020?
By combining ML and NLP for analyzing large sets of data, the process can be automated whilst also creating data democratization, by communicating data insights to colleagues, executives and shareholders with ease.
Augmented analytics allows business to;
- Achieve deeper data analysis – augmented analytics can pinpoint which factors are truly influencing a businesses output
- Receive insights faster – augmented analytics allow insights in a matter of seconds
- Better utilize resources, and
- Gain actionable insights.
Overall, augmented analytics allow businesses to assess their performance, identify growth opportunities and gain a holistic understanding of where a business stands competitively in the market, contributing to a solid business strategy.
Augmented Data Management
Augmented data management (ADM) combines artificial intelligence and machine learning capabilities to allow information management categories to become self-configuring, self-tuning and self-sufficient. Augmented data management allows this to occur by automating a variety of manual tasks, allowing users of any technical skill level to use data more autonomously. ADM converts metadata used for audits, lineages and reporting to powering dynamic systems.
The five areas where ADM is being incorporated to expedite data preparation activities include:
- Data quality
- Master data management
- Data integration
- Database management systems
- Metadata management
Natural Language Processing (NLP) and Conversation Analytics
Natural language processing (NLP) enables computers to understand the human language. NLP gives non-technical business users an easier way to ask query complex data to provide output and explanations of insights. Conversational analytics expands on the NLP concept to enable the same queries to be posed and answered verbally.
Conversational analytics benefits businesses through its artificial intelligence capabilities.The AI navigates through data in a way that enables users to extract the correct data sets from multiple sources and makes it available via voice or type queries. Other benefits of conversation analytics are:
- Time – Conversation analytics removes the need to think about how to receive the data. Users only need to think about what information is needed.
- Accuracy – Human error is removed as machines are programmed to select needed data, aggregate and prepare the data for you.
- Mobility – of conversational AI interfaces means that it is in all devices and a standalone application is not required, allowing users to receive insights whenever they need.
Graph Analytics such as graph processing and graph databases is a set of techniques that are designed to overcome complexity and allow users to understand how entities, such as people, places and things are related to one another. These are techniques that treat the relationships between data as equally as the data itself. Graph analytics holds data without constricting it to a predefined model, which in turn highlights how each individual entity connects or is related to others.
Graph analytics allows businesses to accelerate data preparation and enable more adaptive data science with benefits including consistent performance, flexibility and agility.
- Performance – The performance of graph analytics stays consistent and constant no matter how fast a business data may grow every year.
- Flexibility – Businesses are more flexible when able to dictate changes to existing structures without endangering functionality.
- Agility – Graph technology aligns perfectly with today’s agile, test-driven development practices, allowing graph-database-backed applications to evolve with a business’s changing requirements.
The approach to data through graph analytics means relationships and connections are consistent through all stages of data lifecycle: from idea, to design, to implementation, to operation.
Blockchain is a decentralized database which is shared across a network of computers. This type of database contains a series of unchanged records of data that is not owned by a single entity, rather managed by a cluster of computers.
Each of these blocks of data (block) is secured and bound to each other using cryptographic principles (i.e. chain).
Blockchain technologies address three challenges in data analytics:
- Traceability – each time goods are exchanged on a blockchain, an audit trail is present to trace where the goods came from.
- Transparency – its transaction ledger for public addresses is open to viewing.
- Security – blockchain is more secure than other record keeping systems as it encrypts and links each new transaction to the previous transaction.
Blockchain technology can be practically applied to any industry. A thorough understanding of its implementation will highlight areas where it can be best applied and in what way.
Data Privacy and Security
Data Privacy and Security has shifted its focus from guidance to stepped-up enforcement. The shift is evident in the implementation of the GDPR 20 months ago, the CCPA introduced January 1 2020 , and the moves that countries such as India and Brazil have made to introduce their own Data Protection Bill.
2020 will see an increase in the resources and policies implemented by businesses to effectively meet the requests from customers of the use of their data.