The democratization of data facilitates quicker decision-making, operational effectiveness, financial success, and improved customer experiences.

Data Challenges

We recently spoke with a customer about their challenges, and the most frequent ones were:

• Don’t have access to data.
• No integrated views of the data
• No trust in the data
• Not sure how to use the tools.
• Data SME availability

Why Data Democratization

Data Democratization will enable access to data for different departments or roles. Also ensures users can understand and visualize data in the context of their specific use cases. Some of the following users heavily rely on the data to do their regular activities.

• Sales
• Product and Engineering
• Marketing
• Customer Service
• HR
• Executives

Self-service is essential so users from organizations can explore, visualize and search for information without relying on IT-Teams who are busy and expensive resources.

Also read: Certainty in streaming real-time ETL

Data Governance and Security

Data democratization allows many users from the organization to access the data, and this leads to a potential risk of data security. So, we need to maintain control of the data platform to ensure high security and data governance. Every organization has their own unique method of security and access policy. The policy needs to be updated and kept up to date to have better data governance. Some of the key processes for better governance are:

• Create a guideline for who can access data and the type of data.
• Set the role-based access.
• Perform data discovery and profiling and create the policy based on the profiling reports.
• Set the policy for PII, HIPA and GPDR data.
• Enhance the base policy with encryption, masking, and tokenization across each data service accessed.
• Monitor the data access.
• ·Audit the data access.

Democratizing Data

Here are some of the steps that can be considered for implementing data democratization.

• Study the existing data landscape:
  • Is data on-premises, in the cloud or both?
  • Tools and technologies used to build the data platform.
  • Tools and technologies used to analyze data.
  • Volume of data managed.
  • Number of source systems
  • Level of data maturity

• Understand the stakeholders’ needs, business goals or objectives.
• Involve the business and end users in various implementing phases to achieve their decision making process
• Enable self-service analytics tools where IT involvement is very minimal and makes data governance easy
• Assess the data solutions.
• Address the following in data analytics solution.
• Descriptive Analytics — What Happened:
  • Uses historical data to measure performance. For example, tracking sales revenue.
  • Diagnostic Analytics —What Caused This?
  • Incorporates pattern recognition to help users diagnose why something is happening.
  • Predictive Analytics —The following events will take place:
  • Uses historical data, current trends, and modeling techniques to predict future performance.
  • Prescriptive Analytics —What Can We Improve?
  • Prescriptive analytics suggests future steps you can take to influence the outcomes predicted.

• Provide training to users for newly implemented democratization toolset.
• Make data accessible.

Benefits of Data Democratization

• Deliver trusted data to all employees and help on improved decision making.
• Data team can focus on strategic solutions rather than spending internal processing, which saves time and effort.
• High level data quality makes greater trust in the data and decisions
• Everyone in the organization understands the data and data becomes a second language in the organization.

The post Unlocking the Power of Data Democratization: Empowering Your Entire Organization with Access to Data appeared first on Indium.

Small data consumes less data but is just as insightful because it leverages techniques such as;

• Time-series analysis techniques
• Few-shot learning
• Synthetic data
• Self-supervised learning

Wide refers to the use of unstructured and structured data sources to draw insights. Together, small and wide data can be used across industries for predicting consumer behavior, improving customer service, and extracting behavioral and emotional intelligence in real-time. This facilitates hyper-personalization and provides customers with an improved customer experience. It can also be used to improve security, detect fraud, and develop adaptive autonomous systems such as robots that use machine learning algorithms to continuously improve performance.

Why is big data not relevant anymore?

First being the large volumes of data being produced everyday from nearly 4.9 billion people browsing the internet for an average of seven hours a day. Further, embedded sensors are also continuously generating stream data throughout the day, making big data even bigger.

Secondly, big data processing tools are unable to keep pace and pull data on demand. Big data can be complex and difficult to manage due to the various intricacies involved, right from ingesting the raw data to making it ready for analytics. Despite storing millions or even billions of records, it may still not be big data unless it is usable and of good quality. Moreover, for data to be truly meaningful in providing a holistic view, it will have to be aggregated from different sources, and be in structured and unstructured formats. Proper organization of data is essential to keep it stable and access it when needed. This can be difficult in the case of big data.

Thirdly, there is a dearth of skilled big data technology experts. Analyzing big data requires data scientists to clean and organize the data stored in data lakes and warehouses before integrating and running analytics pipelines. The quality of insights is determined by the size of the IT infrastructure, which, in turn, is restricted by the investment capabilities of the enterprises.

What is small data?

Small data can be understood as structured or unstructured data collected over a period of time in key functional areas. Small data is less than a terabyte in size. It includes;

• Sales information
• Operational performance data
• Purchasing data

It is decentralized and can fit data packets securely and with interoperable wrappers. It can facilitate the development of effective AI models, provide meaningful insights, and help capture trends. Prior to adding larger and more semi-or unstructured data, the integrity, accessibility, and usefulness of the core data should be ascertained.

Benefits of Small Data

Having a separate small data initiative can prove beneficial for the enterprise in many ways. It can address core strategic problems about the business and improve the application of big data and advanced analytics. Business leaders can gain insights even in the absence of substantial big data. Managing small data efficiently can improve overall data management.

Some of the advantages of small data are:

• It is present everywhere: Anybody with a smartphone or a computer can generate small data every time they use social media or an app. Social media is a mine of information on buyer preferences and decisions.
• Gain quick insights:  Small data is easy to understand and can provide quick actionable insights for making strategic decisions to remain competitive and innovative.
• It is end-user focused: When choosing the cheapest ticket or the best deals, customers are actually using small data. So, small data can help businesses understand what their customers are looking for and customize their solutions accordingly.
• Enable self-service: Small data can be used by business users and other stakeholders without needing expert interpretation. This can accelerate the speed of decision making for timely response to events in real-time.

For small data to be useful, it has to be verifiable and have integrity. It must be self-describing and interoperable.

Indium can help small data work for you

Indium Software, a cutting-edge software development firm, has a team of dedicated data scientists who can help with data management, both small and big. Recognized by ISG as a strong contender for data science, data engineering, and data lifecycle management services, the company works closely with customers to identify their business needs and organize data for optimum results.

Indium can design the data architecture to meet customers’ small and large data needs. They also work with a variety of tools and technologies based on the cost and needs of customers. Their vast experience and deep expertise in open source and commercial tools enable them to help customers meet their unique data engineering and analytics goals.

FAQs

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The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.

According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:

Predictions about AI/ML in Business

• Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
• Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
• Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
• Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.

Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:

Use Cases for AI/ML in 2022

• Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
• Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
• Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.

Also Read: 10 Promising Enterprise AI Trends 2022

AI/ML Paving the Road Ahead for Growth

In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.

AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.

The post AI & ML: Forecasts and Trends for 2022 and beyond appeared first on Indium.

Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.

Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.

The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.

Data Modernization

The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.

A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.

For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.

To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.

Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.

Tips For Successful Data Modernization

Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.

Revise Current Data Management Strategy And Architecture

It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.

Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.

A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.

Inventory And Mapping Of Data Assets

If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.

It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.

The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.

Data Democratization A Core Objective

Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.

Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.

It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.

Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.

Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.

Technology Investment

Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.

Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.

It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.

Data Accountability

Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.

In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.

Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.

Summary

It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.

The post 5 Tips For Successful Data Modernization appeared first on Indium.

The stress on improvement in process and quality led to the development of methodologies such as Lean and Six Sigma to increase throughput but was still driven by humans with the technology used only for metrics and advanced analysis.

Though manufacturing companies did derive much benefit from these methodologies, the advent of Industry 4.0 technologies such as cloud, artificial intelligence, and Industrial Internet of Things (IIoT) devices has magnified the benefits manifold.

IIoT has made it possible for manufacturers to create smart factories and integrate systems. This has provided them with a unified data source that enables advanced analytics to identify patterns and trends and facilitate informed decision making.

End-to-end connection of machines right from production to delivery provides manufacturers with visibility improving the formulation of strategies and policies for accelerating growth.

Fast-Paced Adoption of IIoT

The integration of systems enables manufacturing companies to have better control of their inventory and supply chain as well as improve energy management. This naturally leads to cost reduction, resource optimization, increased profitability and overall enhanced operational efficiency due to industrial automation, centralized monitoring and predictive maintenance of assets.

No wonder then that the market for IoT in manufacturing industries is expected to grow at a CAGR of 10.1%, from USD 33.2 billion in 2020 to USD 53.8 billion by 2025, according to a ResearchAndMarkets.com report.

A PwC survey of around 1,000 industrial manufacturers revealed that 71% were already building or testing IoT-related solutions in both active and in-development projects and 68% intended to increase their investment in the next couple of years.

The surveyed companies were investing in better technology infrastructure, data management, workforce culture and change management to reap the benefits of digital transformation.

Benefits of Smart Factories

A smart factory with interconnected systems can automate workflows across functions and manage complex processes with greater visibility and traceability. Some of the key areas where they can see the advantages of IIoT devices include:

1. Predictive Maintenance: The breakdown of machinery and the resulting disruption to production is one of the biggest challenges manufacturing companies face. This causes unexpected delays in addition to the cost of repair. In smart factories, sensors embedded in the machinery provide data that can help analyze machine performance as well as receive alerts in case of any issues or deviations from preset specifications for preventive maintenance. This improves the longevity of the machinery, effects cost savings as well as enables scheduling maintenance in a more planned manner.

2. Product Quality: A piece of faulty equipment can also affect product quality. Embedded technologies can help manufacturers keep their machines well-calibrated to ensure that the machinery is as per specifications and can produce the desired product.

3. Supply Chain Management: The IoT devices can be connected to the ERP or SCM system to track inventory and draw real-time insights about product movement from raw materials to finished goods for a smooth supply chain management. It enables the different departments to have a view of the production process and also removes the need for manual documentation, thereby reducing manual errors and the resultant costs.

4. Safety and Security: Worker safety and security in the plant are becoming important due to regulatory requirements as well as to reassure employees and improve their engagement with the business. IoT systems can make it easier for safety leaders to be alerted in case of any potential hazards and risks and monitor Key Performance Indicators (KPIs) of health and security to not only improve compliance but also make the shop floor safe.

5. Energy Efficiency: Not only is energy one of the highest areas of expenditure for manufacturing companies, but it is also one of the most important areas where conservation is the most needed. IoT devices can help identify inefficiencies at the device level to enable businesses to address them effectively. This can help reduce waste and also meet regulatory standards more efficiently and effectively.

The integration of systems also ensures access to enterprise-wide data that facilitates better visibility into operations and more informed decisions. This provides a competitive advantage in addressing potential challenges before they become a problem and helps managers take a proactive approach rather than a reactive one.

At Indium Software, serving the manufacturing sector has been one of our key focus areas and, over the last decade, we’ve picked up immense expertise in serving fast-growing manufacturing companies in industrial, energy, automotive, and diversified segments.

The core of Industry 4.0 revolves around data. And, Indium’s experience in data management and data engineering are key assets while serving this segment.

Challenges to IoT

IoT comes with its own challenges too: Cost, Security, and Lack of Standards, to specifically name a few points.

Manufacturing companies with legacy equipment may find that customizing their existing machinery to scale up to become an embedded device comes at a cost. However, this can be more cost-effective than investing in new equipment and provide the flexibility they require.

Therefore, identifying the right partner who understands their business and can develop bespoke solutions that enable digital transformation at a reasonable cost would be a prime requirement.

The second is security. As more and more devices get added, the security environment becomes that much more complex. Ensuring encryption and other protection to safeguard data would be the second criterion that a partner should be able to ensure.

Using open frameworks and modern software development tools to write IoT firmware can help overcome the limitations of the lack of standards.

A partner such as Indium Software, with more than two decades of experience in cutting edge technologies, can help manufacturing companies experience painless digital transformation.

Our team of experts has experience in Industry 4.0 technologies, IoT, open frameworks, data engineering, security and testing, which is combined with cross-domain expertise to deliver best-fit solutions meeting the unique needs of our customers.

If you would like to know how we can help you improve your operational efficiency with IoT on your shop floor, contact us now.

https://www.indiumsoftware.com/inquire-now/

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