Retail is one of the most data-reliant industries globally. Inventory databases, customer details, POS systems, websites, social media data, and so much more provide rich data. However, to drive success, all this data must be converted into a dynamic, revenue-generating, and business-transforming asset. That’s where Retail Business Intelligence steps in!

Business Intelligence (BI) is especially prominent in customer service and inventory management. BI helps you organize, analyze, and contextualize business data in retail. It also offers performance measures and company trends, which help business owners make informed decisions. That is pure gold in today’s business environment.

Learn how Business Intelligence can transform your retail business in this 3-minute read!

What is Retail Business Intelligence?

Business intelligence is a type of AI that specializes in business functions. It helps users organize and manage businesses in a more efficient manner. In the retail industry, BI takes the big stage. Its superpower is the ability to automate almost any task. Retail BI has revolutionized the standard of business operations and customer satisfaction. These solutions can optimize assortments, sales trends, marketing campaigns, and more.

Currently, retail stores are also implementing BI and advanced algorithms. After all, a satisfied customer is the best business strategy. The BI solution can analyze demographic data, social media behavior, and purchase patterns. Using this information can enhance the customer experience. This creates a unique and personalized service, paving the way to customer retention and loyalty.

What are the Top Benefits of Using Retail Business Intelligence?

The worldwide BI and business analytics software market will reach nearly 17.6 billion dollars in 2024. There is a good reason for that. Business Intelligence powers business operations with valuable customer insights, optimized inventory management, and boosts marketing strategies. Listed below are the prime advantages of retail business intelligence:

1. Improves Business Operations

Optimization of business operations is the primary benefit of business intelligence in retail. It keeps a consistent record of everything happening within the company. This ensures that there are no errors due to an oversight of details or information. This is best seen in its ability to optimize the company’s inventory.

Retailers can use data analytics to process sales data on product demand. This allows them to make appropriate forecasts and predict upcoming trends. Furthermore, optimizing inventory management also involves preventing overstocking. This reduces inventory costs and enhances order management.

2. A Better Understanding of Customer Behavior

Machine learning learns from previous data to make accurate predictions. ML, as a subpart of business intelligence, enables retailers to study their customers. It collects information about the customers’ shopping habits and social media patterns. This data is vital to enhancing CX (Customer Experience) and optimizing marketing initiatives. Retailers can also use data analytics to create services that meet customer expectations. Analytics can help marketers draft customer progress maps, further providing insights about how to connect with the customer.

Let’s take a look at – How does Walmart use business intelligence?

Walmart being the industry giant, derives data from varied sources. The organization uses BI to analyze customer data derived from online transactions, in-store purchases, events, and more to drive intelligent insights. These intelligent insights reflect customer behavior, shopping patterns, and upcoming and fading trends.  Such rich data can help determine the correlation between customers and various products, giving them more power to decide the shelf life of each product. Now they can effectively manage inventory, improve customer experience, and boost brand value, all while driving more revenue. 

3. Helps Optimize Inventory Management

Inventory management includes storehouse activities related to product handling. Retailers face a variety of issues in this department. The issues range from tracking to overstocking. Investing in data analytics can help retailers deal with inventory management. These systems can maintain optimal stock levels and decrease inventory costs.

BI allows retailers to categorize their merchandise based on selective inventory control (SIC), which is also called ABC analysis. Here, BI analytics shows retailers the products that generate the most revenue. It segregates the inventory into three levels: A, B, and C. Level A is the most valuable, and level C is the least valuable.

4. Improved Merchandising

Business intelligence analytics can also recognize underperforming items. Using this information, retailers can optimize their product stock, which encourages them to find creative ways to promote these products. They can also opt to bring in new trending items and phase out stagnated inventory. BI can also categorize customers based on their sales patterns, which enables retailers to target promotions and pricing strategies.

5. Helps Optimize Store Floor Plans

Shopping can be a daunting process on its own. With the added hassle of unorganized floor plans, it becomes a nightmare. An ideal floor plan should help shoppers find products. It should also market new trending items to promote products. BI software can help business owners design a smooth floor plan. BI analytics works through various data sets and recommends insights. This determines if the selected floor plan has enough product types displayed etc. A good floor plan will make the customer’s shopping experience effortless. 

6. Target Marketing

Marketing has recently become intensively data-driven. This is due to so many social media tools and marketing platforms. To stand out from all the competition, businesses need to have a strategy in place. They should have access to useful insights into consumers’ purchase patterns.

With BI analytics, companies can gain impactful insights into their customers’ preferences. These insights further enable them to make smart marketing campaigns. This also promotes the right merchandise on the appropriate platforms. This way, retailers can use business intelligence insights to make their company strategies more effective.

Here’s a perfect case study. How does Amazon use business intelligence to improve customer experience?

Amazon uses business intelligence to segment customers based on demographics, purchase preferences, and browsing history. This helps them better understand their customers’ purchase patterns, choices, and trends, which powers their marketing strategies. Each of their marketing campaigns is, thus, personalized to target the segmented groups, in turn boosting customer trust, personalization, brand value, and sales. 

7. Boosts Efficiency in the Supply Chain

Efficiency is the most important base in the retail sector. Like cogs in a machine, every business department needs to work in sync. This is the only way to meet optimal efficiency in supply chain management. 

Real-time business intelligence provides top-notch tracking capabilities for supply chain operations. This allows retailers to recognize congestion and enhance logistics. Retailers can streamline supply chain processes and cut costs by running deep analyses. They can optimize inventory management, production procedures, and transportation data. All this is possible through the implementation of Power BI.

How to Transform your Retail Operations with Business Intelligence?

From improving operations and marketing to enhancing supply chain efficiency, retailers are using Business Intelligence in many innovative ways. They are also using BI to analyze market trends, track competition, and create targeted advertising campaigns, ensuring a competitive edge in the market. Here are some ways in which the retail industry uses Retail Business Intelligence. 

1. Analyzing Venue Performance

Staying in touch with the floor is important. Retail BI analysis enables store owners to track floor functions. They can track foot traffic and use it to make many decisions. Metrics such as opening hours and site selection based on foot traffic are very useful. 

Case in point: Lowe’s uses predictive analytics to deliver services to individual zip codes. This way, the right store gets the right type and amount of product.

BI stretches until rent negotiations. It gives insights into a center’s visitation. This includes data such as foot traffic and cross-shopping. A shopping center operator can use this technology to justify a rent increase.

2. Identifying Broader Market Trends

Foot traffic is also used to assess the local demand for products. 

Case in point: Retail BI can analyze the regional markets to analyze the potential for new launches. A manager could use BI to analyze the relative performance of previous stores. They can then choose the right location for a new store.

3. Keeping Tabs on the Competition

The world of retail is very competitive. Keep an eye on your competitor and their performance. Compare your progress to theirs to help forecast future growth. Conduct a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis if needed. It will reveal areas that need improvement. This will also help with budgeting and improve productivity.

4. Quantifying Cannibalization Risks

Location intelligence helps retailers reveal the physical reach of each store. These enable the store owners to decide where to set up the new store and where to reduce the store fleet. 

Case in point: It can be used to optimize store performance by relocating it. Data analysis can show you the competition around you. It can also provide insights on better demographic fits for your establishment. In one case, relocation significantly improved business for a retail chain.

5. Creating Targeted Campaigns

Retaining customers and attracting new customers is the lifeblood of retail. BI helps by assessing customer information and providing insights for advertising campaigns. Foot traffic analytics applied to demographic datasets can also provide advertising insights. Businesses can use this power to create more bottleneck stores.

Case in point: Starbucks uses retail business intelligence software to promote products. The company lures customers into the stores with targeted advertisements. They have found a way to keep the existing customers loyal. They attract new customers simultaneously. 

How can Fingent Help Drive More Success in Retail with BI?

Using cutting-edge technology, Fingent ensures the best outcomes for your retail business. These technologies include machine learning, data analytics, and predictive analytics. Experts at Fingent can create a solution that will help your business make intelligent and well-informed decisions.

Here’s an example of how Fingent developed a top-notch solution for one of the leading retail enterprises. The solution tracks a customer’s digital journey across many milestones and delivers augmented experience insights. We have also created solutions for clients that can convert data sets into visual representations and automate data input procedures. Take a look at the complete case study.

Business Intelligence is revolutionizing the retail industry. Not embracing it will keep you behind! Give us a call, and let’s discuss how to make your retail business intelligent! 

Modernization is a desire to stay relevant and grow. App modernization does just that for businesses. It enables them to stay relevant and grow. In line with that, organizations worldwide are scrambling toward digital transformation. How does that benefit businesses?

App modernization or updating older software for newer computing approaches extends the lifespan of an organization’s applications while allowing it to take advantage of technical innovations. It is like renovating an old house to improve efficiency, safety, structural integrity, and more.

However, there have been occasions when the app modernization project has failed. This has been a concern to most business owners considering digital transformation. Are you one of them? If so, through this blog, you can learn why and how to avoid app modernization failure.

First things first. Does your company need app modernization? If so, why?

Why Your Company Needs App Modernization

Apart from what we discussed earlier in this blog, app modernization integrates new features to keep up with the latest business requirements. Even so, many business leaders wonder if their business needs app modernization. Here is a list of specific reasons for you to consider:

• It allows you to create new features and services that align with your current business needs and goals. In addition, you can personalize these features to ensure long-term value creation.
• Educating new and older employees becomes easier as app modernization can automate tedious and repetitive processes.
• App modernization helps you leverage the benefits of the cloud environment, allowing you to provide fast and efficient customer service.
• Cloud-native applications offer top-notch security features as compared to legacy applications.
• App modernization allows you to add newer features, services, and functions. These, in turn, increase customer satisfaction leading to increased revenue.

Why, then, does app modernization backfire at times? And how can you ensure the success of the project?

Read more: Why modernize your legacy systems? What’s the best approach to legacy systems modernization?

Why Some App Modernization Projects Fail

When an app modernization project fails, it may pose serious problems for your business, including wasted resources and time. Failed app modernization may hinder agility and keep your business from capitalizing on modern technology. It was noted that an alarming 79% of app modernization projects still fail. Why? Surprisingly, the failure is not due to the new system or methodology, nor caused by the modernization vendor. The reasons are much deeper. Here are the top 5 reasons why app modernization projects fail:

1. Ignoring the business process

Modernization efforts fail when the business process (accounting, inventory, finance) is poor, and a business tries to correct it with new systems. That will never work. Businesses must first understand what is slowing them down. Determining the problem is the right foundation for successful app modernization.

2. Implementing new systems without considering drastic business changes

When entirely new systems are implemented without considering the drastic business changes, the employees are forced to adapt quickly. In addition, such practice forces the business to evolve to fit the new software. Instead, the software must adapt to fit the current business process and requirements.

3. Businesses mistakenly assume that they are ready for a complete revamp

Most businesses believe that they are ready to replace the old system with new systems, and the business will run better. Now that is a huge mistake. Completely replacing all systems can send your business and your employees into shock. It may even change how customers interact with the business. A better approach is to modernize in calculated steps.

4. Failing to adopt an agile software

The pandemic helped us see that agility is king. Adopting inflexible applications can have disastrous consequences on your business. If you want to be future-ready, replace rigid applications with agile applications.

5. Failing to consult the end user

Modernization projects that do not involve the IT department and the end user fail as they do not consider how the new solution fits business needs.

Read more: Advancing Business Software Development With Cloud Native

The Secret to A Successful App Modernization Project Journey

Every business that embarks on a modernization project journey faces many challenges. The success of your app modernization efforts comes down to two key components: your IT team and your employees. As teams go through this journey, use these secrets to avoid failure and ensure success.

Secret 1.  Build a cross-functional team

When choosing a team or developing an innovation group, build a cross-functional team that can focus on developing the culture, process, and tools needed to improve the modernization process.

Secret 2.  Toss it, sort it, and commit to it

An effective business principle is to welcome all ideas and sort them, but when a decision is reached, you must commit to it and execute that above all else. Doing so will ensure the progress of the project.

Secret 3. Map out the development workflow

Mapping out a development workflow can help you when you encounter complexities in the project. It helps you look at every tool being used and identify dependencies.  

Secret 4.  Set milestones 

Projects may change. Setting small modernization goals will help you adjust your long-term plans and reduce engineering risk. Focus on many small steps when planning for a major milestone. Doing so will ensure smooth progress from one step to another.

Secret 5.  Prioritize data into categories

If you prioritize data into high, moderate, or low categories, you can decide what safeguards to have for each category.

Secret 6.  Do not sew a new cloth on an old cloak

Take a hard look at your development workflow and identify habits that could derail your app modernization process. Don’t just patch up old problems with a new tool. Remember, these problems can derail new tools, so don’t patch, fix.

Secret 7.  Keep your team in the loop

Your team may find it challenging to develop the knowledge they need to work on the new tools. Keeping them informed about the new tools and training them to navigate new workflows can ensure the success of application modernization.

Several aspects may factor into a successful modernization. That is why modernization endeavors are so challenging. However, implementing the above secrets can help the success of app modernization efforts.

Speaking to an expert can help you understand how to proceed with your app modernization needs. Why not reach out to us at Fingent and discuss your project with our software development experts?

AI is not just a technology; it has become part of daily lives. Thanks to AI, we witness new kinds of development in each sphere of life every day. We can access many points with AI now more than we could ever before. From the research that we do on the internet to the goods that come to our doors, AI is directing our lives and business. It has solved many problems faced by humanity. And, revolutionized technology in all industries.
Coined at Dartmouth College in 1956, Artificial Intelligence (AI) gave succeeding generations a potential for information technology that can benefit humans in a profound way. Apart from bringing efficiencies, AI is also bringing new possibilities. These possibilities continue to create new models and opportunities for businesses that tap into them. Quantum and neuromorphic computing is the way forward.
This blog takes a deeper look into quantum and neuromorphic computing and explores which companies can benefit from these technologies.

A Look at The Future Of AI

The possibilities Artificial Intelligence can create are seemingly endless. Innovations are constantly taking place in the AI world. Given that, many business and information technology leaders are looking to better understand how it will affect their business now and in the future.
Taking a look at the future scenarios for how AI could evolve can help IT leaders demystify this emerging technology.  Two such technologies are called neuromorphic or quantum computing. They offer us a new way forward by fulfilling our AI-related dreams. The quantum computing market alone is expected to reach $2.2 billion by 2026, so it’s definitely worth looking into.
The future of AI also depends on improving the capacity of intelligent systems by using powerful hardware. Hardware such as neuromorphic computing and quantum computing will allow companies to build AI solutions that are extremely fast and can encapsulate more data and knowledge.
For example, a virtual assistant may now have the computing capacity to handle one or two products. But with more powerful hardware, it could cover more products. Advantages in this area are ongoing.
Let’s examine these in detail.
Read more: Answering The Top Burning Questions On AI Implementation For Businesses!

A Deeper Look into Quantum and Neuromorphic Computing 

Neuromorphic computation and quantum computing always seemed that they were years away. The fact is commercial neuromorphic chips and quantum computers are in use today. These two new technologies are going to change what looked like a straight path to Artificial Intelligence.

1. Neuromorphic computation

Neuromorphic computation attempts to imitate the way a human brain works. In simple terms, hardware and software elements of a computer are wired to mimic the human nervous system and cerebral system.
The goal of neuromorphic computation is to create devices that can learn, retain information, and make logical deductions just like a human brain does. It also attempts to imitate the human brain by searching for new information.
However, unlike quantum computing neuromorphic computing does is not that demanding. Where quantum computers need temperatures close to absolute zero, neuromorphic computers can easily work in normal conditions.
This increases its practicality and potential to integrate this technology in all kinds of devices.

2. Quantum computing 

Quantum computing deals with the world of atoms and subatomic particles inside those atoms. Unlike neuromorphic computation, quantum computing works on the principle of quantum entanglement.
So, the smaller the computer gets, the more powerful they seem to become. Quantum computing provided powerful new possibilities.
Though it is more complex than traditional computing, quantum computing opens the door to potentially solving very large and complex computational problems. It can potentially deliver a big computational upgrade for ML and AI computing.
Quantum computers will not replace traditional computers. Instead, they will require the support of traditional computers for their specialized abilities.
Read more: Driving Business Innovation and Success Through Sustainability

Industries That Can Leverage Quantum Computing and Neuromorphic Computation

1. Industries That Can Leverage Neuromorphic Computation

a) Aerospace and defense: Neuromorphic computing architecture can help in pattern recognition, event reasoning, and robust decision-making. It can also aid in adaptive learning and autonomous tasking for energy-efficient agile Air Force platforms.
b) Self-driving cars: Similar to space communications, neuromorphic computing enhances self-driving. In imitation of the human brain, neuromorphic chips attempt to think and learn on their own and then adapt their learning to unexpected scenarios on the road.
While conventional computers run commands sequentially, neuromorphic computers process and store data almost at the same time. This makes self-driving cars more energy efficient. It can also help autonomous vehicles learn skills and execute tasks more efficiently.
c) Healthcare: Neuromorphic platforms can be used for the hardware-based implementation of ML methods in treating Chronic Obstructive Pulmonary Disease (COPD) in home-care environments. Real-time analysis of data can be obtained by bringing data from the backend onto a neuromorphic chip.
Furthermore, securing sensitive medical data on a single chip complies better with patient privacy regulations. Since neuromorphic platforms process data near a patient, it offers a large fault tolerance for medical applications.
Moreover, hardware-based neuromorphic systems require less computational power making them perfect for PoC medical devices.

2. Industries That Can Leverage Quantum Computing

a) Aerospace: By considering an exponential number of variables, quantum computing could help determine the optimal alternative for each route. It can also help find the best way to allocate resources so that the crew and passengers are impacted as little as possible.
b) Healthcare and Pharmaceuticals: Harnessing the power of quantum computing can significantly accelerate the timelines of various stages of the pharmaceutical research and development process. It can help life science companies by accelerating the speed of the pre-clinical phase and reducing the cost of drug development.
c) Finance: Quantum computing will be able to help solve the problems of customers in finance institutes. It can optimize investment portfolios and financial derivatives. It can also enable the institutions to accurately characterize anomalous transactions and rapidly detect fraud.
d) Chemistry: It is likely that quantum computing can be applied to simulate the properties and behavior of new molecular structures in chemistry. It can address the probabilistic challenges of quantum mechanics. In the future, quantum computing is expected to predict molecular properties for new molecules.
Read more: Data and AI: How It Has Transformed Over The Years And Trends To Watch out For!

Conclusion

In the coming years, advances in Artificial Intelligence will reach the superintelligence stage. However, the transition to new technology has already begun.
Many companies are incorporating this unique fixture as it has helped them achieve better sales and forecasts. It has also provided them with growth opportunities. Fingent top custom software development company is confident that you would also benefit from implementing this in your business.
Give us a call and let’s discuss what is best for your business.

Businesses are always on the lookout for ways to optimize processes and gain greater visualization. When processes work efficiently, the output of the product is higher. This leads to workflows that run smoothly with minimum errors and higher capacity. A good reason for the growing popularity of process automation and visualization.
Automation and visualization are the future of the business strategy. Gone are the days of carefully filling in graph paper by hand. Today, process automation and visualization help enterprises up their game by allowing access to real-time models capable of accurately capturing the nuanced data sets.
In this blog, we will expand on how enterprises can up their game with process automation and visualization.

How Enterprises Can Up Their Game with Process Automation and Visualization

Data visualization enables human operators to manage vast sets of data, glean insights from different information sources, and perform operations more intuitively and strategically.
In the current data-immersed world, data visualization can significantly add value by conveying large datasets visually. What does this mean for your business? This means a better grasp of critical customer data.
According to the IDC, the collective sum of the world’s data is predicted to grow to 175 zettabytes by 2025. Processing such large amounts of data can become a problem.
By allowing automation and the right programs to sort out your business data, you can generate graphs. You will be able to use these graphs to up your game in business competition.
Data visualization offers businesses the hope of getting a grasp on data. Fortunately, the human brain can process and recognize trends, identify potential issues, and forecast future development from clear visual displays.
Read more: How Powerful Is Data Visualization With Tableau

Look Out for Upcoming Powerful Trends in Automation and Visualization

1. AI and ML

Artificial Intelligence and Machine Learning render visualization more accurate and efficient. These technologies enable businesses to handle customer feedback without bias. Process automation allows you to sort the feedback in real-time and according to your specifications.

2. Unlock Big Data with Data Democratization

Large amounts of data are hard to understand. It requires data scientists and other experts to unlock its treasures. Not anymore. Advanced no-code data analysis platforms can automate your data process. This is called the democratization of data.
Democratization of data leaves it malleable and easy to display allowing your employees any level of tech support. When this is paired with the data visualization type, it can unlock big data results for teams at all levels of your organization.

3. Video Visualization Is Here to Stay

Young and old alike tend to retain the information they see over what they hear.  This would mean that video infographics will be the future.
Video applications for business strategy and customer retention are key areas for future strategic data visualization implementation.

4. Real-time Visualization for Early detection

Knowing a problem at the exact moment it arises can assist businesses in customer retention and brand presence. Early detection can have a dramatic impact on the bottom line.
Process automation can help run a dashboard that allows users to submit their error reports to your customer support. Then the reviews can be tagged and analyzed using sentiment analysis.

5. Mobile Optimized Visualization

An increased number of people access the internet on their mobile devices. Your business needs mobile-optimized data visualization to stop customer churn.
It enables you to know if your potential customers are learning about your services through social media or an online review board. Though mobile-optimized visualization is an easy step, it is critical to keep your business on top of the game.
Read more: 7 Awesome Data Visualization Tools

Business Applications of Process Automation and Data Visualization

1. Financial Service and Insurance 

The finance service industry is a prime candidate for process automation and data visualization. Two top requirements of this industry are customer response time and compliance with strict regulations.
When automated, quick decisions can be made based on pre-defined rules like loan applications, claims processes. businesses can use data visualization to make reliable predictions or risk calculations in the financial industry.
Insurance fraud can cost billions of dollars damage.  Process automation and data visualization can improve fraud detection.
Read more: Deploying RPA for Finance, Healthcare, and IT Operations.

2. Distribution and Logistics

Process automation and data visualization can minimize costs by planning transport promptly, reducing costs of downtimes and maintenance.

3. Sales

Data visualization can greatly improve relationships with your customers. It helps you know the needs of your customers better, and address each of them directly in real-time.

4. Marketing

Data visualization and process automation can reduce marketing costs substantially.  These technologies can help evaluate the demographics, location, transactions, and interests of your customers. Visualizing these details can help you understand their purchase patterns.
Thus, data visualization can be used to create and target new customer segments. Cross-selling is another advantage. At the same time, data visualization may reveal that customers are dissatisfied. Identifying this and responding quickly can counteract the situation to retain your customer base.

5. Healthcare 

Process automation and data visualization enable cheaper healthcare. It can help predict disease occurrence and proactively propose countermeasures.

6. Science and research

Visualization enables the evaluation of the data of an experiment. Process automation and visualization can be advantageous especially when an experiment generates large amounts of data within seconds.

7. Production

Large amounts of data are generated during production. Using process automation and visualization can help plan preventive maintenance and prevent production delays or downtimes.

Prepare Your Business For The Future With Fingent 

Fingent helps enterprises automate document-based processes. We can help you create safer sharing and collaboration. Our platform allows you to create teams, assign roles and privileges, and streamline communication.
Fingent’s partner integrations allow you to use it together with your existing software. Our top-level measures protect our users’ data. The encryption we provide ensures content integrity and prevents alteration.
Fingent top custom software development company can help your organization reach the goal of paperwork elimination. Doing so can lead to efficient resource distribution throughout the organization.
What’s more, it reduces carbon footprint. Our experts bring along specializations supported by scientific rigor and in-depth knowledge of advanced techniques to design, develop, and deploy solutions for process automation and visualization.
Give us a call today and let’s get talking.

Over 95% of businesses struggle to manage unstructured data in their day-to-day operations. Inability to decipher data prevents them from navigating the market successfully, making business forecasts, and customizing their offerings to match the changing market trends. This proves why data analytics is crucial in enterprise strategy planning. By 2030, the global big data and analytics market value is expected to touch $684.12 billion. As more companies embrace data analytics to enhance customer experience, optimize existing business processes, and lower costs, it’s important to take note of the data and analytics trends that will hold the reins in 2024 and beyond.
Here’re ten trends to behold:  

1. Scalable and Responsible AI

Research and Markets report that AI makes analytics 48% more effective for industry applications. Traditionally, artificial intelligence (AI) techniques were applied to analyze historical data. However, unpredicted events such as the COVID-19 pandemic increase the demand for real-time data analysis. Adaptive machine learning promotes scalable, responsible, and intelligent AI that offers insightful business analytics even with smaller datasets. Scalable AI will enhance learning algorithms, reduce time-to-value, and make business systems and data more interpretable. AI integration will increase the precision of data analysis in 2024.
Read more: 6 Ways Artificial Intelligence is Driving Decision Making 

2. Hybrid, Multi-cloud, and Edge Computing

According to McKinsey, 70% of companies will adopt hybrid or multi-cloud technologies and processes by 2022. Hailed as the hallmarks of distributed IT infrastructures, multi-cloud management and edge computing enable companies to extend their computing capacity to the edge of their networks. This allows businesses to reach more data-hungry devices as the data is analyzed locally, close to the data source. Edge and multi-cloud reduce latency and improve decision-making with advanced, on-demand analytics. Today, every business generates volumes of unstructured data. Relying on traditional batch-based reporting to analyze big data cannot help anymore. 2024 will see the rise of distributed cloud models powered by hybrid, multi-cloud, and edge environments.
Read more: Future-proof Your Business with 5G, Edge Computing, and Cloud

3. Data Fabric Architecture

Data fabric architecture supports businesses to seamlessly navigate the complex digital business landscape that generates a lot of unstructured data every minute. It allows organizations to adopt a modular approach, known as composability, through which organizations can integrate new capabilities or features as low-code, reusable, individual components. Unlike the traditional monolithic architecture, composability allows businesses to integrate new features and changes to their enterprise applications without redoing their tech stacks. According to Gartner, data fabric reduces the deployment time by 30% and maintenance time by 70%. The ability to reuse technologies and capabilities from numerous data hubs, data lakes and data warehouses is expected to go a long way in tailoring analytics experiences.

4. Data Democratization and Self-service Analytics

The rise of low-code/ no-code digital platforms is accelerating the shift to self-service analytics. These platforms empower non-technical business users to access data, garner insights and make faster decisions. Today, self-service analytics is improving business response, enterprise agility, speed-to-market, and decision-making. InfinCE, a low-code workplace orchestration platform enables seamless team collaboration by extending your ability to integrate multiple business apps. Its data-powered business dashboard software supports marketers and non-technical users to analyze data, glean insights, track KPIs, and make strategic decisions. As data becomes the key to unlocking business value, 2024 will see the democratization of data extending beyond the realms of technical analysts and data scientists to ensure better inclusivity.

5. XOps 

The merger of development (Dev) and IT operations (Ops) has given rise to the “Ops trend.” The list of acronyms with the suffix Ops is expanding pretty fast. XOps aims to bring all these terms (DevOps, DataOps, MLOps, ModelOps, etc.) under one umbrella to advance automation and AI adoption, and minimize the duplication of technologies and processes. XOps enables data and analytics deployments to function effectively in tandem with other software fields. In 2024, more data analytics experts will start using XOps to operationalize and automate their processes in conjunction with the software development cycle. This eliminates data management and insights generation challenges from the very beginning of software development. XOps will augment the power of enterprise technology stacks to deliver high-quality on-demand analytics.
Read more: DevOps: Building a New Culture of Software Development and Delivery 

6. Graph Analytics

Gartner estimates that by 2025, 80% of data and analytics innovations will be crafted using graph technologies. Graph analytics employs deep learning algorithms to correlate multiple data points (entities such as people, events, things, locations, etc.) scattered across various data assets by exploring their relationships. This offers businesses a holistic understanding of the market, customer segments, consumer preferences and behavior, logistics, and risks. Graph analytics improves contextual understanding which enables businesses to identify problems and address them faster. SAP HANA is a leading graph database that comes with built-in processing engines to perform context-based data search. It allows users to access the correct data quickly. In 2024, graph technology will be used widely in search engine optimization, fraud and identity detection, supply chain logistics, social network analysis, and so on.
Read more: SAP HANA Helps Unlock Massive Health Data 

7. Small and Wide Data

Until 2020, historical data replicating past conditions was enough to train AI and ML models. Disruptions caused by the COVID-19 outbreak have made such past data obsolete. It means that data analytics professionals should find new ways to use the available data more effectively. “Small data” and “wide data” techniques reduce the volume of data required for training AI models and help extract more value from diverse and unstructured data sources. By 2025, 70% of organizations will switch from big to small and wide data, improving contextual analytics and making AI systems less data-hungry.

8. Decision Intelligence

Decision Intelligence (DI) is a data analytics discipline that analyzes the sequence of cause and effect to create decision models. These decision models visually represent how actions lead to outcomes by observing, investigating, modeling, contextualizing, and executing data. DI helps make faster and more accurate decisions that result in better outcomes. Gartner forecasts that in the next two years, one-third of large corporations will leverage DI to augment their decision-making skills.

9. Generative AI

Generative AI is an artificial intelligence technique that uses existing text, images, and audio files to generate new content. This technique proves to be highly useful in producing new and authentic data that mimics the original in data-scarce situations. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the two key technologies that support Generative AI. By 2025, generative AI will account for 10% of all data produced, up from less than 1% today, states Gartner. In 2024, Generative AI is expected to augment targeted marketing, drug development, and software code creation.

10. Natural Language Processing

If you’re using Google Assistant or Amazon Alexa, you’ve already experienced NLP in action. NLP supports data analytics in multiple ways by leveraging techniques such as speech recognition, machine translation, chatbots, text classification, sentiment analysis, and so on. It offers business leaders, marketers, salespeople, and researchers with the precise insights needed to make better decisions. Reports show that the rising demand for advanced text analytics is driving NLP adoption in sectors like healthcare, social media analytics, and consumer and market intelligence. 2024 will witness the rise of no-code and low-code NLP platforms that will make AI and ML more ubiquitous.

Undoubtedly, data is what we see almost everywhere, and it is enormous. And it doesn’t stop there, it is growing continuously at a level beyond imagination! Let’s have a look at how it has changed over the years.

A look into how Data and AI transformed in years!

In the 1950s, when there were fewer technological developments, companies would collect the data(offline) and analyze it manually. This was also backed by limited data sources that made it time-consuming in obtaining the results.

The mid-2000s paved the way for changing the world for the better and it was during this time the term “big data” was coined. Almost every business that had something to do with digital infrastructure started looking for ways to use the large data and come up with meaningful insights.

This era also saw the invention of tools like Data mining, OLAP, etc., taking technological advancements to the next level. In general, the internet gained immense popularity not only for organizations but also for households. During this time, technology became more advanced and provided automated options for managing data, and data analysts could analyze data, trends, etc., and provide better recommendations.

Google, Amazon, Paypal, and others also made a mark causing the volume of data to reach newer heights. However, all this posed a storage and processing problem.

The late 2000s to early 2010s saw a surge in Facebook, Twitter, Smartphones, and connected devices. The companies used improved search algorithms, recommendations, and suggestions driven by the analytics rooted in the data to attract their customers. Enterprises also realized that would have to deal with unstructured data and so they got familiar with databases such as NoSQL. New Technologies were introduced for faster data processing and machine learning models were used for advanced analytics.

Now, businesses are a step ahead and using automated tools using cloud and big data technologies. With cloud platforms, it is now easier to enable massive streaming and complex analytics.

Read more: 5 ways in which big data can add value to your custom software development

Having seen how data has evolved over the years, let’s have a look at how Artificial Intelligence has transformed in the last generation.

In 1950, a British mathematician and WWII code-breaker- Alan Turing was one of the first people to come up with the idea of machines that could think. To date, the  Turing Test is used as a benchmark to determine a machine’s ability to think like a human. While this notion was ridiculed at the time, the term artificial intelligence gained popularity in the mid-1950s, after Turing’s death.

Later, Marvin Minsky, an American cognitive scientist picked up the AI torch and co-founded the Massachusetts Institute of Technology’s AI laboratory in 1959. He was one of the leading thinkers in the AI field through the 1960s and 1970s. It was the rise of personal computers in the 1980s that sparked interest in machines that think.

That said, it took several decades for people to recognize the true power of AI. Today, Investors and physicists like Elon Musk and Stephen Hawking are continuing the conversation about the potential for AI technology in combination with big data could have and how it could change human history.

AI technology’s promising feature is its ability to continually learn from the data it collects. The more the data it collects and analyses through specially designed algorithms, the better the machine becomes at making predictions.

Impact on business

AI and big data have an impact on businesses like never before. Whether it is workflow management tools,  trend predictions, or even advertising, AI has changed the way we do business. Recently, a Japanese venture capital firm became the first company ever to nominate an AI board member for its ability to predict market trends faster than humans.

On the other hand, data has been the primary driver for AI advancements. Machine learning technologies can collect and organize a large amount of data to make predictions and insights that otherwise cannot be achieved with manual processing. This not only increases organizational efficiency but reduces the chances of any critical mistake. AI can detect spam filtering or payment fraud and alert you in real-time about malicious activities.

AI machines can be trained to handle incoming customer support calls thereby reducing costs. Additionally, you can use these machines to optimize the sales funnel by scanning the database and searching the Web for prospects that have similar buying patterns as your current customers.

Read more: The Future of Artificial Intelligence – A Game Changer for Industries

5 trends in data and artificial intelligence that can help data leaders.

1. Customer experience will be the key

Supply chain and operating costs will mean nothing if you are unable to hold on to your customers. Today, businesses have to be more connected with their customers to be on top of the game. From in-person and digital sales to call centers, companies will have to collect data to have a holistic view of the customer. Businesses must consider other forms of interaction such as using voice analytics to understand how customers interact with call centers or chatbots.

2. Leveraging External data

External data can provide early warning signs about what’s going on. To make external data work, companies must start with a business problem and then think about the possible data that could be used to solve it. That said, companies might need to modernize data flows to leverage external data.

While many businesses have started leveraging external data, some companies haven’t leveraged it yet as they are either too focused on internal data or finding it difficult to transfer data.

A prime example of brands that used external data is Hershey’s Chocolates. It leveraged external data to predict an increase in the number of people using chocolate bars for Backyard S’mores and a decline in sales for smaller candy bars for trick-or-treating.

3. CDOs leading the way towards a data-driven culture

Introducing any new technology without training your employees to adapt and figure out new skills and processes will not be effective. According to Cindi Howson, chief data strategy officer at analytics platform provider ThoughtSpot, Chief Data Officers (CDOs) need to take the lead and empower their employees and the organization to gain time and efficiency with data.  Also, CDOs will have to make sure to upskill employees to take full advantage of new technology.

4. Multi-Modal learning

With advances in technology, AI can support multiple modalities such as text, vision, speech, and IoT sensor data. All this is helping developers find innovative ways to combine modalities to improve common tasks such as document understanding.

For example, the data collected and processed by healthcare systems can include visual lab results, genetic sequencing reports, clinical trial forms, and other scanned documents. This presentation, if done right, can help doctors identify what they are looking at. AI algorithms that leverage multi-modal techniques (machine vision and optical character recognition) could augment the presentation of results and help improve medical diagnosis.

5. AI-enabled employee experience

Business leaders are starting to address concerns about the ability of AI to dehumanize jobs. This is driving interest in using AI to improve the employee experience.

AI could be useful in departments such as sales and customer care teams that are struggling to hire people. Along with robotic process automation, AI could help automate mundane tasks to free up the sales team for having a better conversation with customers. Additionally, it could be used to enhance employee training.

Read more: 9 Examples of Artificial Intelligence Transforming Business Today

Conclusion

Leveraging data and Artificial intelligence has grown due to the pandemic and businesses are digitally connected than before the lockdown.

At Fingent, we equip business leaders with insights, advice, and tools to achieve their business goals and build a future-proof organization. To learn more about how we fuel decision-makers to build successful organizations of tomorrow, contact us.

What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is a statistical approach used to analyze data and produce descriptive and graphical summaries. Analysts may or may not use a statistical model, but EDA primarily foresees what the data can reveal to us beyond formal modeling. 

With EDA you can analyze your data as it is, without the need to make any assumptions. EDA further validates and expands the practice of using graphical methods to explore data. EDA gains insights from statistical theories that give easily decipherable insights. Exploratory data analysis techniques can also be used to derive clues from data sets that are unsuitable for formal statistical analysis.   

Exploratory Data Analysis displays data in such a way that puts your pattern recognizing capabilities to full use. The patterns are evident to an examination that is careful, direct, and most importantly assumption-free. Thus, you can understand relationships among variables, identify problems such as data entry errors, detect the basic data structure, test assumptions, and gain new insights.  

Purpose of Exploratory Data Analysis

The prime purpose of EDA is to study a dataset without making any assumptions. This helps the data analyst to authenticate any assumptions made in devising the problem or operating a particular algorithm. Researchers and analysts can, therefore, recommend new schemes that were not previously considered. 

In other words, you apply inductive reasoning to obtain results. These results may be in opposition to the theories that directed the initial data collection process. Thus, EDA becomes the driver of transformation. This approach allows you to oppose planned analyses and probe assumptions. The ensuing formal analysis can continue with better credibility. EDA techniques have the potential to uncover further information that may open new areas for research.        

Role of EDA in Data Science

We need to understand the role of EDA in the whole process of data science. Once you have all the data, it has to be processed and cleaned before performing EDA. However, after EDA, we may have to repeat the processing and cleaning of data. The cleaned data and results obtained from this iteration are further used for reporting. Thus, using EDA, data scientists can rest assured that the future results would be logical, rightly explained, and relevant to the expected business circumstances. 

EDA helps to clean the feature variables that are to be used for machine learning. Once data scientists get familiarized with the data sets, they may have to go back to feature engineering since the early features may be unable to serve the objective anymore. After completion of the EDA, data scientists obtain a feature set that is required for machine learning. Each dataset is generally explored using multiple techniques. 

Read More: Top 10 Must-Know Machine Learning Algorithms in 2020

Methods of Exploratory Data Analysis

Exploratory data analysis is carried out using methods like:

• Univariate Visualization – This is a simple type of analysis where the data analyzed consists of a single variable. Univariate analysis is mainly used to report the data and trace patterns.
• Bivariate visualization – This type of analysis is used to determine the relationships between two variables and the significance of these relationships.
• Multivariate visualization – When the data sets are more complex, multivariate analysis is used to trace relationships between different fields. It reduces Type I errors. It is, however, unsuitable for small data sets. 
• Dimensionality Reduction – This analysis helps to deduce which parameters contribute to the maximum variation in results and enables fast processing by reducing the volume of data. 

Using these methods, a data scientist can grasp the problem at hand and select appropriate models to corroborate the generated data. After studying the distribution of the data, you can check if there’s and missing data and find ways to cope with it. 

Then comes the outliers. What are your outliers and how are they affecting your model? 

It’s always better to take small steps at a time. So you need to check if you can remove some features and still get the same results. More often than not, companies just venturing into the world of data science and machine learning find that they have a lot of data. But they have no clue how to use that data to generate business value. EDA techniques empower you to ask the right questions. Only specific and defined questions can lead you to the right answers. 

Exploratory Data Analysis: Example with Python

Read More: Why you should migrate to Python 3

 Suppose you have to find the sales trend for an online retailer. 

Your data set consists of features like customer ID, invoice number, stock code, description, quantity, unit price, country, and so on. Before starting, you can do your data preprocessing, that is, checking the outliers, missing values, etc. 

At this point, you can add new features. Suppose you want the total amount. You multiply quantity and unit price to get this feature. Depending on the business requirement, you can choose which features to add. Moving on, by grouping the countries and quantity or total amount together, you can find out which countries have maximum and minimum sales. Using Matplotlib, seaborn, or pandas data frame you can visually display this data. Next, by grouping the year and total amount, you can find out the sales trend for the given number of years. You can also do the same for each month and find you out which time of the year has shown a spike or drop in sales. Using this same method, you can identify further problems and find out ways to fix them. 

Read More: How to Use Data Analytics to Improve Enterprise Sales Numbers

The key to exploratory data analysis is to first understand the LOB and get a good hang of the data to get the desired answers. Get in touch with us to know more about EDA. 

CX Solution to Improve Retail Growth

Nurturing communities and building loyalties is now more critical than ever for all retail brands. With instant access to the latest trends and technologies, customers demand better experiences in their interactions with retail brands across all touchpoints. Hence, Customer experience (CX) has become the most important facet of the retail marketing strategy. Retailers, therefore, have to focus on improving CX through every channel.

Importance of CX solutions

Companies can leverage authentic data and modern technology to transform customer experiences and positively impact their business’ future. While most organizations do have systems in place to track the performances of their CX strategies, few track the end-to-end customer journey. Using appropriate CX solutions, organizations can bridge the gap between expected and actual experiences. CX solutions help companies measure and understand the impact of their CX management strategies. 

By employing CX solutions, you can manage the interactions that current and potential customers can have with your brand, thus enabling you to meet or exceed their expectations. CX solutions leverage customer interactions to align the brand image according to the customer’s perceptions. This helps you foster strong and long term customer relationships.

Related Reading: 5 Ways to Enrich Customer Experience at Your Retail Store

Top Trends in CX

Staying abreast of the latest technologies and trends in Customer Experience will help you stay ahead of the competition. It’s time to hone your CX strategies by following these latest trends that rule the CX market.

• Omni-channel CX: Customer journeys have become more dynamic than ever. Based on convenience, customers constantly switch mediums. Since the line between physical and digital channels are blurring, customers expect seamless experiences in their interactions across all channels. It’s important for retailers to strike a proper balance between the “traditional” and “online” business models based on their customers’ preferences. Adopting omnichannel customer care strategies will help resolve complex issues quickly.

• Artificial Intelligence: CX enhancement requires comprehending vast amounts of chaotic and complex data in real-time at high speeds. This scenario is most suitable for AI-powered solutions. Using AI, you can replicate human-like engagements (chatbots for example), track customer-behavior and roll out customized campaigns on their preferred channel of operation. Thus you turn your data into valuable customer insights.

• Hyper personalization: Customers expect high levels of personalization and prefer to buy from brands that offer services/products that are fine-tuned according to their requirements. With a hyper-personalized approach, retailers can identify subtle customer traits and deliver highly targeted and relevant services. To develop this level of hyper-personalization, your data and analytics have to be aligned to paint a clear picture of your customers’ choices.   

• AR/VR: Augmented Reality (AR) and Virtual Reality (VR) technologies are touted as the “technologies of the future” since they provide highly immersive and engaging customer experiences. AR and VR provide customers with a hands-on experience which helps them make better choices. Many retailers are already reaping the benefits of implementing these futuristic technologies. For instance, Ikea allows customers to check how the furniture would look in their homes before buying using AR. Famous clothing brand Marks and Spencer uses virtual try-on mirrors to boost their store experiences.

• Virtual assistants and chatbots: Virtual assistants and chatbots enable companies to deliver faster and more efficient services at low costs. Some may argue that chatbots lack empathy and hence cannot replace human customer service representatives. However, you should not overlook the fact that advances in AI have given bots the ability to decipher human emotions. By combining the technologies of a virtual assistant and chatbots, you can provide your customers with personalized and empathetic experiences.

Related Reading: Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

Future of CX

Customer Experience will continue to be crucial for brands to survive in a disruptive business environment. Retailers need to adopt agile models to retain customers and attract new ones. Going forward, CX will also depend on employee experiences. If your employees are empowered, they will in turn care for your customers. Your interactions, both with your customers as well as your employees across all channels need to be more meaningful and effective.

https://www.fingent.com/insights/portfolio/smart-product-ordering-system/

Gartner states that 64% of consumers give more importance to their experiences with a brand than to the price of a product or service. Fingent helps you implement the latest technological advancements to make your CX strategies fruitful. Contact us to know more. 

Can Data Warehousing Enhance the Value of Data Visualization & Reporting?

Organizations rely heavily on data to make crucial business decisions. Hence, it is important for your business to have access to relevant data. That is where a well-designed data warehousing comes to your rescue!

Besides gaining actionable insights, corporate executives, business managers, and other end-users make more informed business decisions based on historical data. 

Today’s Analytics and Business Intelligence solutions provide the ability to:

• Optimize business processes within your organization
• Increase your operational efficiency
• Identify market trends
• Drive new revenues
• Forecast future probabilities and trends

Before understanding how data warehousing can add more value to data visualization and reporting, let’s take a look at what these terms mean.

Analytics and Business Intelligence

Business Intelligence is a process that includes the tools and technologies to convert data from operational systems into a meaningful and useful format. This helps organizations analyze and develop meaningful insights to take timely business decisions. The information derived from these tools demonstrate the root cause of your business problems and allow decision-makers to strategize their plans based on the analysis.

Business Intelligence is information not just derived from a single place, but multiple locations and sources. It can be a combination of the external data derived from the market and the financial and operational data of an organization that is meaningfully applied to create the “intelligence”. 

Data Warehouse

Data warehouse is a repository that collects data from various data sources of an organization and arranges it into a structured format. An ideal data warehouse set up will extract, organize, and aggregate data for efficient comparison and analysis. Data warehouse supports organizations in reporting and data analysis by analyzing their current and historical data. This makes it a core component of Business Intelligence.   

Unlike a database, that stores data within, at a fully normalized or third normal form (3NF), a data warehouse keeps the data in a denormalized form. It means that data is converted to 2NF from 3NF and hence, is called Big Data. 

Key benefits of a Data Warehouse

• Combine data from heterogeneous systems
• Optimized for decision support applications
• Storage of historical and current data 

Why We Need Data Warehouse for Business Intelligence?

Before the business intelligence approach came into use, companies used to analyze their business operations using decision support applications connected to their Online Transaction Systems (OLTP). Queries or reports were retrieved directly from these systems. 

However, this approach was not ideal due to: 

• Quality issues 
• Reports and queries were affecting business transaction performance 
• Data resides in heterogeneous sources 
• Non-availability of historical data
• Non-availability of data in the exact form required for reporting

Connecting your organization’s business intelligence tools to a data warehouse can provide you benefits in terms of production, transportation, and sale of products.

Data Visualization vs. Data Analytics – What’s the Difference?

Data Warehousing and Business Intelligence Using AWS 

Today, traditional BI has given way to agile BI where agile software development accelerates business intelligence for faster results and more adaptability. Big Data is growing fast to provide useful insights for making improved business decisions.

There has been a paradigm shift in data storage with warehousing solutions moving increasingly to the cloud. Amazon Redshift, for instance, is one of the most popular cloud services from Amazon Web Services (AWS). Redshift is a fully-managed analytical data warehouse on cloud, that can handle petabyte-scale data, which enables analysts to process queries in seconds. 

Redshift offers several advantages over traditional data warehouses. It provides high scalability using Amazon’s cloud infrastructure to set-up and for maintenance, without the need for upfront payments. You can either add nodes to a Redshift cluster or create additional Redshift clusters to support your scalability needs.

You can use AWS Marketplace ISV Solutions for Data Visualization, Reporting, and Analysis.

Data visualization helps you identify areas that need attention or improvement, clarify factors that influence business such as customer behavior, and making decisions such as finding out a suitable market for your product or predicting your sales volumes, and much more.

TIBCO Jaspersoft, for example, is a solution that delivers embedded BI, production reporting, and self-service reporting for your Amazon data at affordable rates. It features the ability to auto-detect and quickly connect to Amazon RDS and Amazon Redshift. Jaspersoft is available in the AWS Marketplace in both single-tenant and multi-tenant versions. TIBCO Jaspersoft for AWS includes the ability to launch in a high availability cluster (HA) as well as with Amazon RDS as a fault-tolerant repository. Pricing is based on the Amazon EC2 instance, type as well as the chosen single or multi-tenant mode.

Image source: http://bit.ly/2IWWCDn 

Summary

By moving your analytics and business intelligence to a hybrid cloud architecture you will be able to handle huge amounts of data and scale at the rate of expansion required by your business. You will also be able to deliver information and solutions at the speed that your employees and customers demand, and gain insights that will enable your organization to innovate faster than ever.

Business Intelligence and Data Warehousing are two important aspects of the survival of any business. These technologies give accurate, comprehensive, integrated, and up-to-date information on the current enterprise scenario which allows you to take the required steps and make crucial decisions for your company’s growth. To know how your business can benefit from the latest technologies, get in touch with our custom software development experts today!