.gif)
What’s the common thread between Amazon, Netflix, Starbucks, and Uber? All these technology behemoths are data-first industries. They have set up their entire empires on the bedrock of data. The real power of data, if leveraged correctly, can transform start-ups into powerful corporations that can leapfrog many other more prominent players in the market in just a few years.
Data can help enterprises identify ideal prospects, address their needs, engage them better, and drive revenue further. For every business to succeed, they must serve customers to their utmost satisfaction. For this, organizations need to understand their customers’ behaviour to produce products and services just like the way they need. Here’s where a data-first culture comes into play.
Data will be the golden ticket in the future beyond 2022 as well. In 2022, data will empower organizations to respond to customer trends, identify business growth opportunities, and enable them to predict and navigate challenges in a disruptive economy. Smart enterprises will employ best practices to make the best use of data and extract actionable insights out of it:
Modern companies and key decision-makers now understand the value of data in business. They will be investing in AI and ML-led technologies to collect more granular intelligence and capitalize on it. It is expected that around 76% of global businesses will increase their investment in data analytics capabilities in the next two years.
The combination of data management systems and AI/ML is becoming increasingly prevalent. Businesses will be increasingly leveraging AI-enabled data management systems to automate repetitive and complex data management tasks and improve the accuracy and productivity of the entire data value chain. By 2022, around 45% of manual data management operations will be reduced through the use of advanced RPA and AI/ML technologies.
Natural language processing (NLP) and conversational analytics: A subset of advanced AI, NLP, and conversational analytics will enable users to ask questions about data using natural language query (NLQ) and receive visualization and explanation of business intelligence. NLP and conversational analytics will allow AI-based data models to be more human-like and go beyond routine algorithms to deliver personalization.
As enterprises capture data from disparate sources, they unintentionally create data silos that make it difficult to access, interpret, and monitor data and its history within all customer-interacting systems. A semantic data catalogue will help enterprises in climbing this challenge.
Enterprises will be harnessing the knowledge graph model encoding a semantic layer to map relationships between data and standardizing it from disparate sources. It will help data stewards prepare accurate datasets and curate the data for clear understanding. Going forward, it will become central to data management initiatives, i.e., enhancing database governance, data quality, and more.
Advancing into the future, B2B database providers will more frequently leverage tech-enabled whitespace discovery to identify the untapped or unutilized gaps in the existing customer database. It will involve using advanced data analytics to wade through heaps of technographic, firmographic, demographic, and contact data to identify the unmet needs of existing or potential customers.
The whitespace discovery model involves the assimilation of big data volumes and removing the clutter to enrich the data. And in the end, predictive analytics and propensity models will be used to define the lead nurturing buckets, predict the future trend of the data, and enable an insights-based decision-making process.
One of the most prominent trends ensuring data success in 2022 is the sync between data management with marketing outreach programs. Modern enterprises will develop a marketing program to continuously engage and clean the data as a part of the process. Let’s look at some of the future trends that companies are expected to follow to keep their database refreshed and relevant:
Data profiling: It will help businesses segment, track, and continuously refresh relevant influencers and decision-makers within their existing databases. Data profiling will also enable them to capture net new entity and contact data from the ideal customer profile ecosystem.
Data maintenance: The first step to effective data management is to standardize data obtained from disparate sources. Going forward, automated tech-based data management will be key to eliminating duplicate, inconsistent, or inaccurate data from the system. Enterprises will leverage automated data standardization, data cleansing, and data deduplication engines.
Data validation: Another crucial trend to keep the database updated, accurate, and relevant will be automated data validation and refresh practices. It will help validate and enrich the database with new insights enabling businesses to build targeted and personalized marketing campaigns.
With the increasing importance of data in businesses along with advancement in data capture and storage methods, undoubtedly, the future of database management is automation. Leveraging AI/ML and NLP-led technologies, companies will be able to effectively simplify complex and repetitive tasks and manage their data to gain a strategic advantage over their competitors. Large businesses will continue leveraging data analytics to positively impact their topline and bottom line. Others will embed it in their business processes to build actionable strategies and navigate future challenges in a data-driven economy.
Follow us on Google News
