Today businesses that are able to leverage data can stay competitive in a data-driven world and unlock new opportunities for success. Businesses of all sizes are recognizing the immense value of data analytics.

By harnessing the power of data, organizations can gain valuable insights into their customers, operations, and market trends. This knowledge can drive informed decision-making, improve efficiency, and ultimately enhance business performance.

This comprehensive article will explore some of the most essential data analytics techniques that businesses can employ to extract meaningful information from their data.

1. Descriptive Analytics

Descriptive analytics is the foundation of data analysis. It involves summarizing and describing data to gain a basic understanding of its characteristics. This technique is essential for identifying patterns, trends, and outliers within the data.

Key techniques

Data Aggregation: Compiling data from different sources to create comprehensive summaries. For example, a retail company might aggregate sales data across different regions to identify top-performing stores.

Data Visualization: Creating charts, graphs, and other visual representations to make data more understandable and accessible. Tools like Tableau, Power BI & customized dashboards allow businesses to
create interactive visualizations that make data easier to interpret.

Frequency analysis: Determining the frequency of occurrence of different values within a dataset. For e.g. user behaviour can be tracked in terms of views, likes, time etc. This data can be used to rank content for users.

Descriptive statistics: Calculating measures such as mean, median, mode, standard deviation, and variance to summarize data distribution. This can help companies plan in advance for e.g. festive discounts on ecommerce.

Descriptive analytics helps businesses understand historical performance, identify trends, and make informed decisions based on past data. It provides a solid foundation for forecasting and strategic planning.

Take for e.g. a retail company uses descriptive analytics to analyze sales data and identify the best-selling products, popular customer segments, and seasonal trends.

This information helps the company optimize inventory management, target marketing efforts, and improve customer satisfaction. It reduces inventory carrying costs and improves bottom line for their business.

2. Diagnostic Analytics

Diagnostic analytics delves deeper into the “why” behind the patterns and trends identified through descriptive analytics. It involves investigating the underlying causes of specific phenomena and identifying potential root causes.

Key techniques

Root Cause Analysis: Identifying the underlying causes of problems or anomalies. For instance, if a company experiences a sudden drop in sales, root cause analysis might reveal issues like a flawed marketing strategy or supply chain disruptions.

Correlation Analysis: Examining relationships between different variables to understand how they influence each other. This technique can reveal insights such as how customer satisfaction impacts repeat purchases.

Hypothesis testing: Evaluating whether observed data is consistent with a particular hypothesis.

A manufacturing company uses diagnostic analytics to analyze production data and identify the root causes of quality defects.

By understanding the factors that contribute to defects, the company can implement corrective measures to improve product quality, reduce costs & product recalls.

By analyzing return reasons and customer feedback, companies can pinpoint issues with products or services and implement corrective actions to improve customer satisfaction.

3. Predictive Analytics

Predictive analytics leverages historical data and statistical models to forecast future outcomes. It is used to identify potential risks and opportunities and make informed decisions about future actions.

Key techniques

Regression Analysis: Modeling the relationship between variables to predict future outcomes. For example, a company might use regression analysis to forecast sales based on historical data and market trends.

Time Series Analysis: Analyzing data points collected or recorded at specific time intervals to identify patterns and make forecasts. Retailers often use time series analysis to predict demand for products during different seasons.

Machine learning: Building models that can learn from data and make predictions without being explicitly programmed. By analyzing historical sales data & external factors like weather patterns, Walmart can forecast product demand & adjust inventory levels accordingly.

Data mining: Discovering patterns and relationships within large datasets.  It involves using various statistical & computational algorithms to extract valuable information that can be used to make informed decisions.

The accuracy of predictive analytics depends on the quality of the data and the algorithms used. While predictive models can provide valuable forecasts, they are not foolproof and should be used in conjunction with other decision-making tools.

Financial institutions use predictive analytics to assess credit risk and determine the likelihood of loan defaults. This information helps the institution make more informed lending decisions and manage risk more effectively.

4. Prescriptive Analytics

Prescriptive analytics goes beyond prediction and provides recommendations for optimal actions. It combines data analytics, business rules, and optimization techniques to suggest the best course of action based on specific goals and constraints.

Key techniques

Optimization modeling: Formulating and solving mathematical models to find optimal solutions. Using mathematical models to find the best solution to a problem. Take for instance, logistics companies use optimization to determine the most efficient delivery routes.

Simulation: Creating models of complex systems to test different scenarios and evaluate potential outcomes. Businesses can use simulation to evaluate the impact of various strategies and make data-driven decisions.

Decision support systems: Providing tools and information to support decision-making processes. They combine data analysis techniques with a user-friendly interface to support decision-making processes.

Prescriptive analytics helps businesses make informed decisions by providing actionable recommendations. It enhances decision-making by evaluating various scenarios and suggesting the best course of action.

A transportation company uses prescriptive analytics to optimize route planning and vehicle scheduling. By considering factors such as traffic conditions, customer demand, and driver availability, the company develops efficient and cost-effective transportation plans.

By analyzing factors like demand, competition, and operational constraints, transport companies can recommend pricing strategies and scheduling adjustments that maximize revenue and efficiency.

5. Statistics in Data Analytics

Statistics play a crucial role in data analytics by providing the tools and methods needed to analyze data and draw meaningful conclusions. Here are two important statistical concepts that are widely used in data analytics:

Techniques in Real-Time Analytics

Probability: Probability measures the likelihood of an event occurring. It is used to quantify uncertainty and assess the risk associated with different outcomes.

Hypothesis testing: Hypothesis testing is a statistical method used to evaluate whether observed data is consistent with a particular hypothesis.

It involves setting up a null hypothesis and an alternative hypothesis and then using statistical tests to determine whether the data provides sufficient evidence to reject the null hypothesis.

Modeling the relationship between variables. For example, real estate portals use predicting house prices based on factors like size, location, and number of bedrooms etc.

6. Real-Time Analytics: Making Immediate Decisions

Real-time analytics involves analyzing data as it is generated to make immediate decisions. It is crucial for businesses that need to respond quickly to changing conditions.

Techniques in Real-Time Analytics

Stream Processing: Analyzing data in real time as it flows into the system. This technique is used for applications like fraud detection and network monitoring.

Event-Driven Analytics: Responding to specific events or triggers in real time. For example, online retailers use event-driven analytics to offer personalized promotions based on customer behavior.

Ecommerce companies uses real-time analytics to create discounts for users and adjust pricing dynamically. By analyzing data on user demand, availability of items, and market conditions. Companies can make immediate adjustments to price to improve their sales growth.

7. Customer Analytics: Understanding Customer Behavior

Customer analytics focuses on analyzing customer data to understand behavior, preferences, and trends. It helps businesses tailor their products, services, and marketing strategies to meet customer needs.

Techniques in Customer Analytics

Segmentation: Dividing customers into groups based on shared characteristics. This allows businesses to target specific segments with personalized marketing efforts.

Customer Lifetime Value (CLV): Calculating the total value a customer brings to a business over their lifetime. CLV helps businesses prioritize high-value customers and allocate resources effectively.

Retailers use customer analytics to enhance their loyalty program and personalize marketing. By analyzing customer purchase history and preferences, retailers can offer targeted promotions and recommendations that drive customer engagement and loyalty.

Implementing real-time analytics requires investing in data processing technologies and infrastructure that can handle high-velocity data streams. Tools like Apache Kafka and AWS Kinesis are popular for real-time data processing.

Conclusion

Data analytics has become an indispensable tool for businesses seeking to gain a competitive edge in today’s data-driven world.

By effectively utilizing techniques such as descriptive, diagnostic, predictive, and prescriptive analytics, organizations can extract valuable insights from their data and make more informed decisions.

Kreyon Systems offers comprehensive data analytics solutions tailored to your organisational needs.  If you have any queries, please contact us.

The post Data Driven Enterprise: Essential Data Analytics Techniques appeared first on Kreyon Systems | Blog | Software Company | Software Development | Software Design.

The cloud has revolutionized how businesses store, manage, and analyze data. Its inherent scalability and elasticity offer a compelling solution for handling ever-growing data volumes and complex analytical needs.

But simply migrating data to the cloud doesn’t guarantee a scalable architecture. Designing scalable data architectures in the cloud requires careful planning and adherence to best practices.

Businesses are generating and collecting vast amounts of data at an unprecedented rate. From customer interactions and transactional records to sensor data and social media feeds, the volume, velocity, and variety of data continue to grow exponentially.

To harness the potential of this data deluge, organizations are turning to cloud computing, which offers unparalleled scalability and flexibility for storing, processing, and analyzing massive datasets.

Here, we’ll explore the key principles and strategies for designing scalable data architectures that leverage the power of the cloud.

Understanding the Importance of Scalability

Before delving into best practices for designing scalable data architectures in the cloud, let’s first understand why scalability is crucial. Scalability refers to the ability of a system to handle increasing workloads and growing datasets without sacrificing performance or reliability.

In today’s dynamic business environment, where data volumes and user traffic can fluctuate unpredictably, scalability is essential for ensuring that data-intensive applications remain responsive & available.

A scalable data architecture can seamlessly adapt to fluctuations, ensuring optimal performance & responsiveness. There are two key aspects to consider:

Horizontal Scaling: Adding more resources (compute power, storage) to existing systems to distribute the workload.
Vertical Scaling: Upgrading existing resources (CPU, RAM) within a single system.

Cloud platforms excel at horizontal scaling, allowing you to add resources on-demand without significant downtime. This flexibility is a game-changer for data-driven businesses.

Assess Your Data Landscape

A clear understanding of your current data ecosystem is paramount. This includes:

Data Sources: Identify all the sources your data originates from, including internal applications, external APIs, and sensor data.
Data Types: Understand the variety of data you handle, such as structured, semi-structured, and unstructured.
Data Usage Patterns: Analyze how data is accessed, processed, and utilized within your organization.
Data Partitioning: Choose appropriate partitioning keys based on data characteristics and access patterns. For example, time-based partitioning is effective for time-series data, while hash-based partitioning evenly distributes data across shards.
a) Partitioning: Logically divide your data into smaller subsets based on a defined criteria (e.g., date range, customer segment). This improves query performance and simplifies data management.
b) Sharding: Distribute partitioned data across multiple servers (shards) for horizontal scaling. This enables parallel processing and reduces the load on individual servers.

Partitioning and sharding strategies require careful planning and can vary depending on your specific data model and access patterns.

By mapping your data flow, you can identify potential bottlenecks and areas for improvement, paving the way for a scalable architecture.

Key Considerations for Designing Scalable Data Architectures

When designing scalable data architectures in the cloud, several key considerations should be taken into account:

Scalability Goals: Clearly define your scalability goals and objectives. Determine the anticipated data volumes, throughput requirements, and performance expectations. Consider factors such as data growth rate, peak usage periods, and geographic distribution of users.
Data Storage: Choose scalable storage solutions that can accommodate growing datasets and provide high availability and durability. Cloud-native object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage offer virtually unlimited scalability and can store petabytes of data cost-effectively.
Data Processing: Decouple storage and compute layers to enable independent scaling of each component. Leverage serverless compute services such as AWS Lambda, Google Cloud Functions, and Azure Functions for processing data in a scalable and cost-efficient manner. These services automatically scale based on workload demand and eliminate the need to provision and manage infrastructure.
Data Partitioning: As your data volume grows, managing it as a single unit becomes unwieldy. Partitioning and sharding techniques come to the rescue: Implement data partitioning strategies to distribute data across multiple storage nodes or shards. Partitioning allows for parallel processing and improves query performance.

Managed Data Services Using Cloud Native Technologies

Managed data services on cloud platforms are fully managed, scalable, and highly available services that are designed to handle specific data-related tasks and workloads without requiring customers to manage the underlying infrastructure.

These services abstract the complexities of provisioning, configuring, and maintaining data infrastructure, allowing organizations to focus on their core business objectives rather than managing IT operations.

Take advantage of managed data services offered by cloud providers for specific data processing tasks. Services such as Amazon Redshift, Google BigQuery, and Azure SQL Data Warehouse are optimized for scalability and performance and handle tasks such as data indexing, partitioning, and optimization automatically.

Managed data services typically include features such as automated backups, high availability, security, and performance optimization.

Cloud providers offer a vast array of services specifically designed for scalability and elasticity. Businesses can utiilise the distributed nature of cloud computing to design architectures that can scale horizontally.

Leverage these services whenever possible:
Cloud Storage: Utilize managed storage solutions like object storage (e.g., Amazon S3, Azure Blob Storage) for cost-effective and highly scalable data warehousing. These services provide virtually unlimited storage capacity and can accommodate growing datasets effortlessly.
Managed Databases: Cloud-based databases (e.g., Amazon RDS, Azure SQL Database) offer automatic scaling capabilities, simplifying infrastructure management.
Data Integration and ETL: Managed data integration and ETL (Extract, Transform, Load) services such as AWS Glue and Azure Data Factory provide fully managed platforms for building, orchestrating, and automating data integration workflows.
Big Data Processing: Managed big data services such as Amazon EMR (Elastic MapReduce) and Azure HDInsight offer fully managed platforms for running big data processing and analytics workloads.

By adopting cloud-native technologies, you benefit from built-in scalability features and avoid the complexities of managing on-premises infrastructure.

Store data in scalable object storage services and use serverless compute services such as AWS Lambda, Google Cloud Functions, or Azure Functions for processing. This serverless approach eliminates the need to provision and manage infrastructure, enabling automatic scaling based on workload requirements.

Monitoring and Optimization:

Monitoring and optimization in a scalable data architecture are critical for ensuring efficient operation, security, performance, and cost-effectiveness.

Performance Monitoring: Constantly monitor the performance of your data architecture to identify bottlenecks, latency issues, or areas of inefficiency. This includes monitoring system resources such as CPU, memory, disk I/O, and network bandwidth.
Query Performance: Monitor the performance of database queries and data processing jobs. Identify slow-performing queries and optimize them by creating appropriate indexes, partitioning tables, or rewriting queries.
Resource Utilization: Keep track of resource utilization across your data infrastructure, including database servers, storage systems, and processing clusters. Ensure that resources are allocated efficiently and scale them up or down as needed to meet changing demands.
Data Integrity and Consistency: Implement monitoring mechanisms to ensure data integrity and consistency. This includes detecting and resolving data anomalies, ensuring data quality, and maintaining consistency across distributed data stores.
Data Lifecycle Management: Implement monitoring for data lifecycle management, including data ingestion, storage, processing, and archival. Monitor data retention policies, data aging, and data purging to optimize storage costs and ensure compliance with regulatory requirements.

By focusing on these aspects of monitoring and optimization, you can ensure that your scalable data architecture operates efficiently, performs well, and meets the needs of your organization while minimizing costs and risks.

Kreyon Systems is a trusted partner for building scalable data applications tailored to meet your unique business needs. If you have any queries, please reach out to us.

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The data needs to be formatted in a specific way for it to be leveraged by ML algorithms. The quality of the datasets is paramount to providing pertinent insights for the organisation. When dealing with large volumes of unstructured datasets, there could be issues with missing values, obsolete data, invalid formats. outliers etc. So, for any algorithm to produce relevant, useful and contextual predictions, data preparation is a must. If data is not cleansed and validated properly, it can affect the accuracy of the system and even provide misleading insights. Here’s a look at the pivotal steps for good data preparation to build more accurate systems.

1. Defining the Problem

The first step in data preparation requires defining the context in which data will be used. It needs clarity in terms of the key issues or problems that need to be addressed. For e.g. an organisation that is focused on improving its turnaround time for product development will need to analyse the project implementation steps.

The breakup of the project schedule and identifying the parts that can be completed without any dependencies can be taken up in parallel. So, the model can provide relevant and contextual tasks to the team involved in the execution. The impact of each task on the project, service delivery and its quality can be assessed by mapping the relevant data.

The focus needs to be well defined in terms of the outcomes an organisation wants to achieve. In the above case, it could be improving product development time by 30% and quality by 30%. The steps involved are then mapped as data inputs for the algorithm to suggest improvement measures. By focusing on the problem and KPIs, the objectives of the system are clear. It can simplify considerations about the types of data to gather for analysis. 

The intended purpose and key outcomes drive the design of the machine learning mode. Once the problem is well formulated, it is easier to map relevant data. The problem could be defined using some of these steps: 

i)    Gather data from the relevant domain or case in point.

ii)   Let the data analysts and subject matter experts weigh in the system

iii)  Select the right variables to be used as inputs and outputs for a predictive model for your problem.

iv)  Review the data that is collected.

v)   Summarize & visualise the data using statistical methods.

vi)  Visualize the collected data using plots and charts for building predictive models.

2. Data Collection & Discovery

The process of transforming raw data into actionable data sets for algorithms and analysts requires consolidation of data. There could be many sources for business data, structured or unstructured. These could be endpoint data, existing enterprise systems, customer data, marketing data, accounting and financial data etc. 

Data preparation requires mapping all the data sources as well as identification of relevant data sets. The behaviour of the model to make practical insights depends on the data sets. It may be pointed out that adding too much irrelevant information adversely affects the accuracy of the model. 

To start with a list of key performance indicators or questions that need to be answered are analysed. The relevant data sources are mapped, integrated and made accessible for analysis.

3. Data Cleansing

Data cleansing helps to streamline information for analysis. The validation techniques for data cleansing can be used to identify and eliminate inconsistencies, aberrations, outliers, invalid formats, incomplete data etc. Once the data is cleansed, it can provide accurate answers upon analysis.

There are tools that can help organisations to clean up their data and validate it before using it for machine learning. Good quality data is the backbone of an accurate machine learning model. Data preparation involves cleaning up, validating data formats, check missing values, and other things that can affect data analysis.

Data cleansing also involves proactively looking at outliers or one time events in data sets. For e.g. correlation between online sales and lockdowns and identifying their correlation using ML models. The idea is to understand the causal relations inherent in data, but eliminate outliers that can affect the accuracy of the system. There are open source tools like Open Refine that may be used for standardising your organisational data. 

4. Data Format & Standardization

After the data set has been cleansed, it needs to be formatted and standardised. This step involves resolving issues like multiple date formats, inconsistent datatypes, removing irrelevant information, duplicity, redundancy, removing multiple sources of truth etc. 

After data is cleansed and formatted, some data variables may not be needed for the analysis and hence they can be deleted. Data preparation requires deletion of noise and unwanted information for building a robust automation system.

The cleansing and formatting process should have a consistent and repeatable work flow. It can be used by the organisation to maintain consistency of data in the future iterations too. The data is constantly added to the model realtime with similar steps. For e.g. marketing data could be added every month based on relevant keyword searches on the internet.

5. Data Quality 

Do you trust the quality of your data? Erroneous data can lead to disastrous consequences. When the data is not reliable, it can create more problems than it solves. Take for e.g. an online retailer who needs to dynamically price the items on its portal, any inaccuracy in pricing may affect sales as well as reputation for the retailer.

Low quality data is a deterrent to the design of a good machine learning model. Even with the best algorithms and models, the system could produce ordinary results, when data quality is poor. But, what makes good quality data? The answers may vary across industries and companies. Industries like pharmaceuticals and medical need very stringent data quality standards compared to other industries like consumer goods.

An e.g. of Data Quality Assessment Framework adopted by IMF for data quality follows: 

Integrity: Statistics are collected, processed, and disseminated based on the principle of objectivity. 

Methodological soundness: Statistics are created using internationally accepted guidelines, standards, or good practices. 

Accuracy and reliability: Source data used to compile statistics are timely, obtained from comprehensive data collection programs that consider country-specific conditions.

Serviceability: Statistics are consistent within the dataset, over time, and with major datasets, as well as revisioned on a regular basis. Periodicity and timeliness of statistics follow internationally accepted dissemination standards. 

Accessibility:  Data and metadata are presented in an understandable way, statistics are up-to-date and easily available. Users can get a timely and knowledgeable assistance.

Some important questions to ask regarding the quality of your data: 

Is the data reliable and representing realtime information?
Is the data obtained from the right source?
Is the data missing or omitting something important?
Is the data representing sufficient information for you to make a decision?
Is the data representing the relationships between key variables accurately?

6. Feature Engineering & Selection

Feature engineering deals with adding or modifying attributes to model’s output. This is the last stage in data preparation for building a machine learning model.

The feature engineering identifies the most important or relevant input data variables for the model.  It involves deriving new variables from the available dataset based on adjusting and reworking the variables to enable models to uncover useful insights  & causal relationships. The variables or predictors are tweaked to ensure better predictive performance of the system and this is known as feature engineering.

The experimental approach explores different variables from the available data sets to make predictive insights. Some variables may look promising, but may not deliver the right results due to extended model training, overfitting and less weightage in relation to the predictive accuracy of the model.

Many features may need to be evaluated and weighed before converging to the right model. Good data preparation delivers high-quality and trusted data for improving the predictive behaviours and accuracy of the enterprise software.

Kreyon Systems provides enterprise software implementation for clients with end to end data lifecycle management. Our expertise is leveraged by governments and corporates for managing their data. If you have any queries, please reach out to us.

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The data footprint gives companies the leverage to access contextual information for taking the actions. By adding analytical and pattern recognition techniques, organisations can improve the quality of their products and services.

The embedded analytics equips organisations with real time actionable information they need to make their decisions. The information is tailored to the client use cases for e.g. creating marketing campaigns for optimising lead generation, sales conversions charts, ordering inventory items, dispatching ecommerce products to customers etc. Here’s an overview of how embedded experience elevates superior experience for customers: 

1. Tools for Custom Analytics 

Every organisation is run differently, they have specific processes and methods to accomplish their business goals. The self service customer analytics capabilities helps customers to design their own dashboards. For e.g. a sales manager could see the work of his direct reports in terms of calls made, proposals sent and revenue earned for the week. The sales manager and the organisation can design their dashboards as per the information that is most relevant for them. 

The use of custom analytics adds interactivity, intelligence and actionable insights for specific business use cases. With self service analytics functions, companies can design graphs, charts, data metrics and their ranges as per their needs. Simple drag and drop options can be used for designing the dashboards. These dashboards can be tailored to the specific needs of the role and the most important use cases for them.

The visualisation of information can be a great enabler for key decision makers. It builds an interactive, data driven and agile enterprise, which is geared to achieve their key business outcomes. Importantly, customers can use the no code tools to build the interfaces for their business without any external help.

2. Multi Cloud & Hybrid Environment

As organisations continue to use myriad applications for running their business, data layer for these applications could be spread across different cloud providers. For e.g. an organisation could have some of its legacy data on premise, and certain application data on cloud servers like AWS, Google, Azure etc. The embedded analytics could be used to link the data sources and configure customers applications as per their day to day needs. 

The multi cloud and hybrid infrastructure layers can be integrated to give end to end visibility for client applications. The clients need not bother about the underlying technology providers and can get a complete overview of their business data. 

The servers could be hosted on different cloud servers, operating systems and technology infrastructure, but the integration makes everything seamless. The application can be configured with respect to the security and privacy of data as per defined organisational user privileges. An integrated SSO(single sign on) functionality is implemented for the users where they can see data across applications.

3. Data Cleansing

Many organisations especially the government enterprises suffer from duplication of data. There is a large amount of data that may not be up to date, could be inaccurate and needs to be cleansed.

Data cleansing is used for standardising the data, it is used for removing the unwanted data that may be inaccurate or obsolete. The data cleansing process makes use of information from old legacy databases, files, tables, manual records etc. The data is validated, standardised and deduplicated before it is used by the application.

The data quality drives the application and an organisation’s performance today. Old and obsolete information can cripple an organisation. The redundancies and inconsistencies of legacy application data needs to be streamlined before using it. The data cleansing efforts can work on structured and unstructured data to eliminate errors during integration efforts.

After data cleansing is done, good data quality ensures consistent reporting and elimination of redundancies in the organisation. The data reconciliation and cleaning up efforts provide a company with rich information to take the decisions with confidence.

4. AI & ML Tools

The AI and ML tools are used for deriving actionable insights driven by learning obtained from data. For e.g. inventory manager can get a report on the predictive prices for a product based on historical purchase prices and compare it to the online prices. 

AI and ML tools can be used for more accurate forecasting for sales, lead conversions, and accounting etc. The data footprint is being analysed for patterns and learning is used for providing intelligent reports for companies. Marketing campaigns can use AI to identify the right customer segmentation, use online data for learning about client needs and provide qualified leads to the sales teams. 

AI algorithms are used by companies to come up with new insights. The data patterns are used for constructive insights inline with company goals. The chatbots and conversational tools can be used by teams to query the information they’re looking for. In one of the organisations, we implemented a system where the AI system recommends training by creating a win-win situation for the company as well as the employee.

The insights reports can be sent to the management with inputs on how they can improvise their business to meet the desired outcomes. 

5. Map Client Outcomes

A good analytics software maps the customer’s desired outcomes to the product or application. Take an e.g. of a lending company that needs to process underwriting claims. The objective of the lending company is to fast track the underwriting process and disburse loans to the applicants.

The embedded analytics will display the average no. of pending applications at any given time, it will also show the average time for processing an application. Suppose the outcome of the lender is to disburse loan application within 3 days, then it can take actions based on the data metrics to process applications.

Overall, embedded analytics makes organisations take actions faster based on the data. The loan application can be analysed and assessed within minutes to help executives take the decision to approve or reject the application. A good design helps companies map their desired outcomes with data. So, if the lender wants to reduce the application approval time to 3 days, they can track the data and act swiftly to meet their targets driving superior performance.

6.  Domain Expertise

Figuring out your client needs is the key to successful implementation of embedded analytics. Innovative solutions are often delivered by teams, which can work with domain experts to implement the right solutions for them. 

Your customers know their needs better than you. But you can help them to use technological solutions that alleviate their pain points. When the team knows the criteria for a client’s success, they can build the solutions accordingly. Domain and industry expertise is useful to understand the criteria, key outcomes clients are looking for.

The domain expertise from these clients can be used for building breakthrough analytics solutions. Product or service innovation can be prioritised in line with the customer needs. When technology teams and domain expertise work together, it can create breakthrough solutions for them.

The post Embedded Analytics: How to Build a Superior Application Experience with Embedded Analytics appeared first on Kreyon Systems | Blog | Software Company | Software Development | Software Design.

Today’s business world revolves around business data, and it has grown to become the most potent tool in industries across the globe. Data has become such an essential tool that over 40% of brands around the world are planning to enhance their data-driven marketing strategy, according to this study. 

We now have access to advanced technology that makes it easier than ever to collect and analyze data. This post is going to shed some light on the top tips for receiving and getting the most from your business data. But first, let’s look at what makes business data so valuable. 

Why is Business Data so Important?

Gathering data has become the single most powerful tool that businesses use to create massive, extraordinary success. It shows how consumers interact with specific companies and how they would respond to certain factors. In short, data shows us how to create the best customer experience possible. Businesses have spoiled consumers, so they are used to getting what they want from their favorite brands. 

Also, data helps businesses develop better marketing strategies. In the past, companies would just toss a lot of money at television advertising, radio, and newspaper ads and hope some of it stuck. But our access to data has now removed the guesswork. We can know what types of marketing work and can develop a marketing strategy around that information. 

Top 7 Tips to Get the Most out of Business Data

All industries are being affected by data and its disruptive nature. The truth is that companies of all sizes are looking for business analytical solutions to help them separate themselves from the competition. With that said, let’s look at some fantastic tips that will help you get started on the right track. 

1. Develop a Process and Follow Through with It

Business processes are essential to keeping everything organized, so this is always the first step to developing a business data strategy. Outline the specific methods you’ll be using to collect data. Then have a plan for organizing and processing that data. This process must be clear and consistent. Outline every step right down to how your marketing team will use the data to focus their strategies. 

2. Stay Consistent Throughout

Consistency is essential to working with data because you’ll have to ensure that your process is being followed continuously. If you are asking customers to fill out a form with each purchase, then make sure you are consistently requesting it. If surveys are part of your follow up process, then be sure you have a system in place that follows up with every customer. My point here is that you can’t just create a process, follow it for a few weeks and then forget about it. Data analytics solutions are going to be a part of your standard business practice from this point forward.

3. Define the Types of Business Data is Needed

All businesses are already acquiring massive amounts of data. It can be quite overwhelming to keep track of it all. Fortunately, there’s no point in processing all data. All businesses will have different key metrics that they need to keep track of, so it’s essential to define these metrics. That way, you’re not wasting resources by processing irrelevant data.

4. Make Data Collection as Simple as Possible

Most businesses rely on consumers to provide them with essential information regarding the customer journey. But there is an old business adage that says, “if you confuse them, you lose them.” In short, don’t confuse your customers, or they will not cooperate. Provide secure data collection methods. Also, there are cases where you don’t even need surveys. Technology allows us to track customer behavior on a business website.

5. Don’t Rely on Just One Data Collection Method

There’s no single system that is going to be capable of gathering all of the business data required to provide insight. Businesses must incorporate multiple forms of data collection tools if they want to gain the most from their data strategy. Many types of automated software will make it much easier to gather data and follow up with consumers. The point is that you cannot rely on just one method. You need to pick your data from multiple systems to ensure integrity.

#6: Don’t Ignore the Analytical Phase

Once you have collected raw data, you will need an analytical process in place to transform it into useful information. This critical stage is where businesses benefit from the raw data they have collected, so it must not be taken for granted. Analytical processes look for patterns in data and then communicate those findings with teams so that action can be made. This is one of the reasons why data analysis services have become in demand lately. 

7. Never Stop Finding Ways to Improve your Data Strategy

So many businesses will put a data plan in place and then never touch it again. However, that’s a huge mistake because the business landscape evolves at an astonishing rate. Take a step back to evaluate your data strategy. Ask yourself what’s working and then find ways to build on those things. Measuring, analyzing, and improving are the holy trinity of business marketing, so I encourage you to follow this same process when it comes to data. Improve your business data strategy by weeding out methods that are not converting and enhancing ones that are proven to succeed. 

Carefully Weigh your Data Management System

By this point, small business owners have had the benefits of data analysis beaten into their heads, so ignorance is no longer the issue at hand here. The biggest obstacle is that small businesses are simply overwhelmed and don’t have the resources to make the most of their data. The solution is to utilize a data management system to stay in control of this overwhelming process. 

Business data is a tool for unlocking the full potential of your business. Kreyon Systems is helping companies to make the most of their data & make the right decisions. If you need any assistance, please get in touch with us.

Author Bio:

Gracie Myers is a content & research analyst at Research Optimus. She has expertise in Business Research, Market Research, Business Analytics and many more.

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