Implementing Big Data and AI: Best Practices and Strategies for 2023

Implementing Big Data and AI: Best Practices and Strategies for 2023
Discover the latest strategies and best practices for implementing big data and AI into your organization for 2023. Gain insights on leading Big Data and AI solution providers to drive business growth.

Contents

1  Establishing a Relationship between Big Data and AI
2  Importance of Big Data and AI in 2023
3  Key Challenges in Implementing Big Data and AI
4  Best Practices and Strategies for Big Data and AI Implementation
5  Top AI and Big Data Companies to Look For in 2023
6  Conclusion

1.  Establishing a Relationship between Big Data and AI

The relationship between AI and big data is mutually beneficial, as AI requires vast amounts of data to enhance its decision-making abilities, while big data analytics benefits from AI for superior analysis. This union enables the implementation of advanced analytics, such as predictive analysis, resulting in the optimization of business efficiency by anticipating emerging trends, scrutinizing consumer behavior, automating customer segmentation, customizing digital campaigns, and utilizing decision support systems propelled by big data, AI, and predictive analytics. This integration empowers organizations to become data-driven, resulting in significant improvements in business performance.

2.  Importance of Big Data and AI in 2023

In the year 2023, it is anticipated that the utilization of big data analytics and artificial intelligence (AI) will profoundly impact diverse industries. The investment in big data analytics will be primarily driven by the need for data compliance, security, and mobilization, ultimately aiming to achieve real-time analysis. Therefore, businesses seeking to excel in this area must be prepared to adopt cloud technology and make significant advancements in computing power and data processing methods.

Recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks.

(Source: Forbes)

The banking, retail, manufacturing, finance, healthcare, and government sectors have already made substantial investments in big data analytics, which have resulted in the forecasting of trends, enhancing business recommendations, and increasing profits.

In addition, AI technology will make significant advancements in 2023, including democratization, making it accessible to a broader user population. This shift will enable customers to wield authority, and businesses will be able to use AI to better meet their specific and individualized business requirements. Finally, a significant shift likely to be witnessed in the AI field in 2023 is the move to a more industrialized, embedded type of architecture, where actual business users may begin utilizing algorithms.

According to a recent study, 61% of respondents believe that AI will have a significant impact on their industry within the next three to five years.

(Source: Deloitte Insights Report)

3.  Key Challenges in Implementing Big Data and AI

97.2% of business executives say their organizations are investing in big data and AI projects.

These executives cite their desire to become “nimble, data-driven businesses” as the reason for these investments, as 54.4% say that their companies’ inability to do this was the biggest threat they faced.

In addition, 79.4% say they’re afraid that other, more data-driven companies will disrupt and outperform them.

(Source: Zippia)

Implementing big data analytics and artificial intelligence (AI) presents various challenges that businesses must tackle to realize their full potential. One such obstacle is the intricate nature of the data, which could be either structured or unstructured and necessitate specialized tools and techniques for processing and analysis. Moreover, companies must ensure data quality, completeness, and integrity to facilitate accurate analysis and decision-making.

Another substantial challenge in implementing big data and AI is the requirement for skilled personnel with expertise in data science, machine learning, and related technologies. To stay up-to-date on the latest tools and techniques, companies must invest in ongoing training and development programs for their employees. Ethical and legal concerns surrounding data privacy, security, and transparency must also be addressed, especially after recent data breaches and privacy scandals.

Integrating big data and AI into existing IT systems can be a challenging and time-consuming process that necessitates careful planning and coordination to ensure smooth integration and minimize disruption. Lastly, the high cost of implementing these technologies can be a significant barrier, especially for smaller businesses or those with limited IT budgets. To overcome these challenges, companies must be strategic, prioritize use cases, and develop a clear implementation roadmap while leveraging third-party tools and services to minimize costs and maximize ROI.

4.  Best Practices and Strategies for Big Data and AI Implementation

24% of companies use big data analytics. While 97.2% of companies say they’re investing in big data and AI projects, just 24% describe their organizations as data-driven.

(Source: Zippia)

4.1  Building a Data Strategy

One of the biggest challenges in building a data strategy is identifying the most relevant data sources and data types for the organization’s specific business objectives. The sheer volume and diversity of data available can further complicate this.

The key to addressing this challenge is thoroughly assessing the organization’s data assets and prioritizing them based on their business value.

This involves:
  • Identifying the key business objectives and
  • Determining which data sources and data types are most relevant to achieving those objectives


4.2  Implementing a Data Governance Framework

Establishing a data governance framework involving all stakeholders is crucial for ensuring agreement on data quality, privacy, and security standards. However, implementing such a framework can be daunting due to the divergent priorities and perspectives of stakeholders on good data governance. So, to overcome this challenge, clear guidelines and processes must be established:

  • Creating a data governance council
  • Defining roles and responsibilities
  • Involving all stakeholders in the development and implementation of guidelines
  • Data quality management, privacy, and security processes should be established to maintain high data governance standards

Organizations can improve the effectiveness of their data governance initiatives by aligning all stakeholders and ensuring their commitment to maintaining optimal data governance standards.


4.3  Leveraging Cloud Computing

It is essential to carefully select a cloud provider that aligns with the organization's security and compliance requirements. In addition, robust data security and compliance controls should be implemented:

  • Establishing data encryption and access controls
  • Implementing data backup and recovery procedures
  • Regularly conducting security and compliance audits

By following these practices, organizations can ensure their big data and AI projects are secure and compliant.


4.4  Developing a Data Science and AI Roadmap

The obstacles to developing a data science and AI roadmap lie in identifying the most pertinent use cases that cater to the specific business objectives of an organization. This difficulty is further compounded by the potential divergence of priorities and perspectives among various stakeholders concerning the definition of a successful use case. Hence, it is imperative to establish unambiguous guidelines for identifying and prioritizing use cases that align with their respective business values.

This entails:
  • Identifying the key business objectives
  • Carefully ascertaining which use cases are most pertinent to realizing those objectives
  • Meticulously delineating the success criteria for each use case


4.5  Leveraging Established Agile Methodologies

Leveraging well-established agile methodologies is critical in successfully implementing large-scale big data and AI projects.

  • By defining a precise project scope and goals, prioritizing tasks, and fostering consistent communication and collaboration, enterprises can effectively execute AI and big data analytics initiatives leveraging agile methodologies.
  • Such an approach provides teams with a clear understanding of their responsibilities, facilitates seamless communication, and promotes continuous improvement throughout the project lifecycle, resulting in a more efficient and effective implementation.


4.6  Prototyping Through Sandboxing

Establishing clear guidelines and processes is crucial to overcome the challenge of creating prototypes through sandboxing that are representative of the production environment and can meet the organization's requirements.

It includes:
  • Defining the scope and objectives of the prototype,
  • Meticulously selecting the appropriate tools and technologies
  • Guaranteeing that the prototype is an authentic reflection of the production environment

Additionally, conducting thorough testing and evaluation is necessary to ensure that the prototype can be scaled effectively to meet the organization's needs.


5.  Top AI and Big Data Companies to Look For in 2023


H2O.ai

H2O.ai is a leading provider of artificial intelligence (AI) and machine learning (ML) software. It provides a platform for businesses to use artificial intelligence and data-driven insights to drive innovation and growth. The software offers a suite of tools and algorithms to help users build predictive models, analyze data, and gain insights that inform business decisions. With a user-friendly interface and a robust set of features, H2O.ai is a valuable tool for businesses looking to leverage the power of machine learning to stay ahead of the competition.


ThoughtSpot

ThoughtSpot is a leading search and AI-driven analytics platform that enables businesses to quickly and easily analyze complex data sets. The platform offers a range of features, including advanced analytics, customizable visualizations, and collaborative capabilities. It is designed to make data analytics accessible to anyone within an organization, regardless of technical expertise. The platform is also highly customizable, allowing businesses to tailor it to meet their specific needs and integrate it with their existing data infrastructure.


Treasure Data

Treasure Data is a cloud-based enterprise data management platform that helps businesses collect, store, and analyze their data to gain valuable insights. Its platform includes a suite of powerful tools for data collection, storage, processing, and analysis, including a flexible data pipeline, a powerful data management console, and a range of analytics tools. The platform is also highly scalable, capable of handling massive amounts of data and processing millions of events per second, making it suitable for businesses of all sizes and industries.


Denodo

Denodo is a leading data virtualization software company that provides a unified platform for integrating and delivering data across multiple sources and formats in real time. The platform offers unmatched performance and unified access to a broad range of enterprise, big data, cloud, and unstructured sources. It also provides agile data service provisioning and governance at less than half the cost of traditional data integration. In addition, its data virtualization technology simplifies the complexity of data sources and creates a virtual layer of data services accessible to any application or user, regardless of the data’s location or format.


Pendo.io

Pendo.io is a leading cloud-based platform that provides product analytics, user feedback, and guidance for digital products. It allows businesses to make data-driven decisions about their products and optimize their customer journey. The platform empowers companies to transform product intelligence into actionable insights rapidly and at scale, enabling a new generation of businesses that prioritize product development.


TigerGraph

TigerGraph is a graph database and analytics platform that allows businesses to gain deeper insights and make better decisions by analyzing connected data. It is designed to handle complex data sets and perform advanced graph analytics at scale. The platform offers a range of graph analytics algorithms that can be applied to a variety of use cases, including fraud detection, recommendation engines, supply chain optimization, and social network analysis.


Solix Technologies, Inc.

Solix Technologies, Inc. is a leading big data management and analysis software solution provider that empowers data-driven enterprises to achieve their Information Lifecycle Management (ILM) goals. Its flagship product, Solix Big Data Suite, provides an ILM framework for Enterprise Archiving and Enterprise Data Lake applications utilizing Apache Hadoop as an enterprise data repository. In addition, the Solix Enterprise Data Management Suite (Solix EDMS) helps organizations implement database archiving, test data management, data masking and application retirement across all enterprise data.


Reltio

Reltio is a leading provider of cloud-based master data management (MDM) solutions that enable organizations to create a unified view of their data across all sources and formats. The platform combines MDM with big data analytics and machine learning to provide a single source of truth for data-driven decision-making. The solution offers a range of features, including data modeling, data quality management, data governance, and data analytics.


dbt Labs

dbt Labs is a cloud-based data transformation software platform that helps analysts and engineers manage the entire analytics engineering workflow, from data ingestion to analysis. The platform enables users to transform and model raw data into analysis-ready data sets using a SQL-based language. With its modular and scalable approach, dbt Labs makes it easier for data teams to collaborate and manage their data pipelines.


Rockset

Rockset is a real-time indexing database platform that allows businesses to run fast queries on data from multiple sources without needing to manage the underlying infrastructure. It supports various data types, including structured, semi-structured, and nested data, making it flexible and versatile. In addition, the serverless platform is built on a cloud-native architecture, making it easy to scale up or down as needed. With Rockset, users can build real-time applications and dashboards, perform ad hoc analysis, and create data-driven workflows.


6. Conclusion

The relationship between big data and AI is mutually beneficial, given the fact that AI requires copious amounts of data to refine its decision-making capabilities, while big data analytics derives immense value from AI for advanced analysis. As a result, the integration of big data analytics and AI is projected to profoundly impact diverse industries in 2023. Nevertheless, adopting these technologies poses multifarious challenges, necessitating businesses to adopt a strategic approach and develop a comprehensive implementation roadmap to optimize ROI and minimize expenses. Ultimately, the successful implementation of big data and AI strategies can enable organizations to become data-driven, culminating in substantial improvements in business performance.

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data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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