Data & Analytics | Key Trends in 2019
2018 has been a great year for business intelligence. The proliferation of internet-based technologies led to a data explosion and the concept of big data became even more prevalent. Businesses gained access to the cloud, interactive business dashboards replaced spreadsheets, and much more occurred. Now, data quality services and management practices, data discovery techniques, and predictive and prescriptive analytics tools will be among the top trends impacting analytics and business intelligence in 2019.
The new year will also witness multi-cloud strategies and artificial intelligence. Machine learning and AI will transform entire industries, clearing the path for virtual aides and a countless number of cases for automation. The Internet of Things (IoT) will become more intelligent, revealing tremendous potential for smart homes and smart cities. An increasingly productive human-machine interaction will be established with natural language substituting specific commands.
In this blog, we will briefly discuss the major breakthroughs expected in 2019 – the way Mastech InfoTrellis sees them – and how they are expected to modify the new business landscape.
Breakthroughs to Expect in 2019
Artificial Intelligence & IoT
The steady improvement in machine learning and AI technologies will make each business become more data-driven. AI and machine learning are remodeling the way we associate with analytics and data management. The fact is that these technologies will have an impact on our lives, in any case. Internet of Things (IoT) will be the trend. Business houses will rely on more data points to gather data for more detailed business insights.
Predictive & Prescriptive Analytics
Predictive analytics provides predictive modeling assisted by auto-recommendations and auto-suggestions to simplify use and allow business users to leverage predictive algorithms without the mastery and ability of a data scientist. Predictive analytics shows what may occur later on with an adequate dimension of dependability, including elective situations and hazard evaluation.
Prescriptive analytics goes a step further into what is to come. It looks at information to figure out what choices ought to be made and which steps are to be taken to accomplish an expected objective. It is characterized by techniques such as graphical analysis, simulations, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics can help you enhance planning, generation, stock, and inventory network configuration to convey what your clients need in the most upgraded way.
Among various predictive analytics strategies, two are quite popular among data scientists: Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA).
Augmented analytics allows business users to perform early prototyping and test hypotheses without the skills of a data scientist. Smart data discovery tools will ensure user adoption and data democratization with self-serve data preparation and smart data visualization. Gartner says this will soon become a widespread feature of data preparation, management, analytics, and business process management, leading to more citizen data scientists.
The Role of the CDO & CAO
Data and analytics are getting to the core of every business. Every company has had a Chief Information Officer who administered all the information management assets and security issues. Today, however, the volume of data and analytics, and related roles, are getting so huge that new positions developed: the CDO (Chief Data Officer) and CAO (Chief Analytics Officer). Today, the importance of these roles has increased, and they now have one of the toughest seats at the executive table.
Accelerated Migration to Cloud Data
Many companies have embraced this shift, and with this shift, analytics, too, will naturally and obviously follow, in line with the concept of data gravity. Data gravity recommends that administrations and applications are pulled toward the path where information lives.
Actionable Data will Put Analytics in Context
Present day data workers need to complete their tasks with maximum efficiency. They need to get to the information and take to activities in a similar work process. BI platforms are addressing these needs by converging with core business operations, processes, and workflows through capabilities like mobile analytics, embedded analytics, APIs, and dashboard extensions. 2019 is expected to enable more organizations to utilize data analytics precisely where it is required and receive the majority of its rewards.
Open-source analytical languages like R, and projects associated with statistical computing and graphics, have seen wide adoption in the past year. This trend is expected to quicken in the new year. Expect to witness more free, cloud-based data and software tools become accessible for enterprises. Start-ups and small enterprises will benefit the most from this trend in the coming year.
Humanizing Data Analytics through Advancements in Natural Language Processing
Natural Language Processing (NLP) integrates linguistics and computer science to help computers comprehend human language. Natural language programs are developing to support analytical conversations. This means that a human can have a conversation with a system about data in a way as though the system is a human itself.
Edge Computing & Analytics
Edge computing takes advantage of proximity by processing information as physically close to sensors and endpoints as possible, thus reducing dormancy and traffic in the network. Edge computing and analytics can also help enhance security due to a decentralized methodology, which localizes processing and reduces the need to send data over networks or to different processors.
Analyzing and interpreting a huge amount of data can be a challenging and time-consuming task if we use only existing technology. Quantum computing is a solution that will diminish processing time and help companies settle on convenient choices for more desired outcomes.
Customer Analytics & Experience
Customer analytics and continuous experience will be one of the focus areas of business insights trends in 2019. A report states that, “Customer journey analytics, emotion detection, speech analytics, Customer Engagement Center (CEC) interaction analytics, analytics for customer intelligence are anticipated by service leaders to envision and associate the customer venture over numerous gadgets and channels.” By predicting customer behavior, the use of data will become an essential part of the customer experience and formula to success.
2019 Trends in Data & Analytics
Data is the foundation for analytics, and organizations need to analyze all forms of data. Mastech InfoTrellis – a leading Data & Analytics services provider – has leveraged years of analytics experience to develop industry/function-specific next-generation models and consumption layer tools to quick-start analytics and increase adoption.
- A shift to cloud platforms and workloads
- The development of smaller micro-analytics packages that are deployed into applications or workflows
- Increased deployment and exploitation of machine learning algorithms to continuously learn, adapt, improve, and automate
- Ample amount of work related to regulatory implications for data management and governance
- Increased near-shoring and offshoring of analytical work
- Development of multi-technique-based models in support of a variety of business needs
- Big data and integration between digital data and offline data sources
- Customer analytics via customer experiences
- Pre-integrated, pre-built functionalities that include pre-built data models, natural language processors, synthesis and matching, machine learning, analytic inferences, service APIs
- Customer journey analytics, emotion detection, Customer Engagement Center (CEC) interaction analytics, customer sentiment analytics, social media analytics, web-rooming and showrooming analytics
- Turn customer data into meaningful intelligence for a variety of business use cases
- Prescriptive and process-oriented outputs
- Combination of historical data with real-time process data to predict the unfolding of different scenarios and to adapt to them accordingly
- Analytics platforms integrated with data and next-gen consumption apps to manage models and enable ML/AI capabilities
- Centralized analytics hub for measurement and forecasting to promote cross-functional collaboration
- Forward-looking reports with ML-based anomaly detection, causal analysis, and alerts.