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How Businesses Are Reaping the Benefits of Implementing an Enterprise Data Warehouse

Throughout the past few years, Enterprise Data Warehouse systems (EDW) have become one of the most critical elements of modern decision support systems. Their main advantages consists in bringing together data from different sources – not available in the appropriate form in the operational systems, for example, missing historical data – in one central location and arranging them for analysis or drawing together data from diverse sources that may have different formats. 

Moreover, it is a landscape that is growing significantly in complexity and acting as the source of structured data , unstructured data and even Big Data . Thanks to the use of an Enterprise Data Warehouse system, the typical risks inherent in heterogeneous data warehousing that most organizations are faced with, i.e. losing track, increasing data redundancy, and long decision-making paths, can be avoided effectively. All relevant partial data from the most critical data sources along your organizations entire value chain are brought together in a way that enables fast and purposeful decision-making at all organizational levels. Numerous types of information, for example, on production, suppliers, products, partners, stock levels, staff, sales and customers, are all combined in the data warehouse system to provide a holistic view.

Attributes of Enterprise Data Warehouse 

The following four attributes may be considered to give a granular view of an EDW and how it differs from an ordinary data warehouse.

The first attribute of an EDW is that it should have a single version of the truth. The entire goal of the warehouse’s design is to come up with a definitive representation of the organization’s business data and the corresponding rules. Given the variety and number of systems and silos of company data within any organization, many business warehouses may not qualify as an EDW.

Secondly, an Enterprise Data Warehouse should have multiple subject areas. To have a blended version of the truth for a company, an EDW should contain all subject areas concerned to the enterprise such as sales, marketing, finance, human resource and others.

Thirdly, an Enterprise Data Warehouse should be implemented as a Mission-Critical Environment. The entire underlying infrastructure should handle any unforeseen tedious conditions because failure in the DW means loss of income and revenue and stoppage of the business operation. An EDW must have high availability features such as database structural changes or online parameter, business continuance such as disaster recovery and failover and security features.

Finally, an EDW should be scalable across numerous dimensions. It should be expected that an organization's main objective is to grow and that the warehouse should easily handle the growing data complexities of processes that will come together with the progression of the business enterprise.

Let’s look at the Benefits of Enterprise Data Warehouse (EDW)

In the current scenario, nearly every department within a business can avail benefits from data-driven insights. Here are a few business requirements that EDWs address.

1. Real-time access to actionable data 

Enterprise Data Warehouses make data actionable and viewable and in real-time by favoring an ELT approach over the once common ETL paradigm. Data was cleansed and transformed on an external server before loaded into the DW. With an Extract-Load-Transform approach, raw data is extracted from its source & loaded without any change into the DW, making it much faster to analyze and access.

2. Complete understanding of customer

Enterprise Data Warehouses enable a holistic view of a business’s customer, assisting to minimize churn, improve campaign performance, and eventually grow revenue. An EDW also enables predictive analytics, where teams utilize scenario modeling and data-driven forecasting to inform business and marketing decisions.

3. Ensuring and tracking data compliance

Enterprise Data Warehouses facilitate data customers to vet data sources and audit directly and find errors swiftly. A modern EDW can help enable compliance with the EU’s GDPR without implementing an involved process to check multiple data locations.

4. Boosting users with limited technical knowledge

An Enterprise Data Warehouse benefits non-tech employees in job functions beyond finance, marketing, and SCM. For instance, store designers and architects can improve the CX within new stores by delving into data from IoT devices placed in current locations to know which parts of the retail footprint are most or least engaging.

5. Collating data to a single, reliable repository

Modern DW technology allows organizations to store data across cloud providers and different regions. Business users can query an EDW as though it were a unified global data set.

Implementing an Enterprise Data Warehouse Solution

Several software providers offer enterprise data warehouse architecture solutions. Still, for something that fits incredibly with your existing processes and systems, you will be better off building your own. This is not as daunting a prospect as it might appear to be. Kicking off with a reliable CRM platform, users can effectively design a working data warehouse that is entirely compatible with their organizations and on par with anything being offered on the market. Knowing the concept behind the enterprise data warehouse (EDW) goes hand in hand with understanding the requirements of your business. So, before you commit to any particular data warehouse solution—or build your own—do your research.

 Wrapping up

Understanding the implementation of an enterprise data warehouse can help you determine what actually fits your data platform needs.  Organizations who believe in setting up a warehouse may take years of testing and planning because of the scale and its most typical form. As a business user, you might be tricked by the number of technologies and options utilized, so it’s crucial to consult with professional experts in the field of data warehousing, ETL, and Business Intelligence. They can assist you with the technical aspect, to define the business purpose, and speak with the ones who will use the actual data in their work. So, get in touch with our experts at Polestar solutions to identify your goals and data requirements. After all, with all the advantages of implementing enterprise data warehouse solutions, it only makes sense to do it right. 

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How are organizations winning with Snowflake?

Cloud has evolved pretty considerably throughout the last decade, giving confidence to organizations still hoping on legacy systems for their analytical ventures. There's an excess of choices for organizations enthusiastic about their immediate or specific data management requirements.

This blog addresses anyone or any organization looking for data warehousing options that are accessible in the cloud then here you are, its Snowflake - a cloud data platform, and how it nicely fits if you are thinking of migrating to a new cloud data warehouse.

The cloud data warehouse market is a very challenging space but is also characterized by the specialized offerings of different players. Azure, AWS Redshift, SQL data warehouse, Google BigQuery are ample alternatives that are available in a rapidly advanced data warehousing market, which estimates its value over 18 billion USD.

To help get you there, let's look at some of the key ways to establish a sustainable and adaptive enterprise data warehouse with Snowflake solutions.

#1 Rebuilding

Numerous customers are moving from on-prem to cloud to ask, "Can I leverage my present infrastructure standards and best practices, such as user management and database , DevOps and security?" This brings up a valid concern about building policies from scratch, but it's essential to adapt to new technological advancements and new business opportunities. And that may, in fact, require some rebuilding. If you took an engine from a 1985 Ford and installed it in a 2019 Ferrari, would you expect the same performance?

It's essential to make choices not because "that's how we've always done it," but because those choices will assist you adopt new technology, empower, and gain agility to business processes and applications. Major areas to review involve- policies, user management, sandbox setups, data loading practices, ETL frameworks, tools, and codebase.

#2 Right Data Modelling 

Snowflake serves manifold purposes: data mart, data lake, data warehouse, database and ODS. It even supports numerous modeling techniques like - Snowflake, Star, BEAM and Data Vault.

Snowflake can also support "schema on write'' and "schema on read"." This sometimes curates glitches on how to position Snowflake properly.

The solution helps to let your usage patterns predict your data model in an easy way. Think about how you foresee your business applications and data consumers leveraging data assets in Snowflake. This will assist you clarify your organization and resources to get the best result from Snowflake.

Here's an example. In complex use cases, it's usually a good practice to develop composite solutions involving:

Layer1 as Data Lake to ingest all the raw structured and semi-structured data.

Layer2 as ODS to store staged and validated data.

Layer3 as Data Warehouse for storing cleansed, categorized, normalized and transformed data.

Layer4 as Data Mart to deliver targeted data assets to end consumers and applications.

#3 Ingestion and integration

Snowflake adapts seamlessly with various data integration patterns, including batch (e.g., fixed schedule), near real-time (e.g., event-based) and real-time (e.g., streaming). To know the best pattern, collate your data loading use cases. Organizations willing to collate all the patterns—where data is recieved on a fixed basis goes via a static batch process, and easily delivered data uses dynamic patterns. Assess your data sourcing needs and delivery SLAs to track them to a proper ingestion pattern.

Also, account for your coming use cases. For instance: "data X" is received by 11am daily, so it's good to schedule a batch workflow running at 11am, right? But what if instead it is ingested by an event-based workflow—won't this deliver data faster, improve your SLA, convert static dependency and avoid efforts when delays happen to an automated mechanism? Try to think as much as you can through different scenarios.

Once integration patterns are known, ETL tooling comes next. Snowflake supports many integration partners and tools such as Informatica, Talend, Matillion, Polestar solutions, Snaplogic, and more. Many of them have also formed a native connector with Snowflake. And also, Snowflake supports no-tool integration using open source languages such as Python.

To choose the prompt integration platform, calculate these tools against your processing requirements, data volume, and usage. Also, examine if it could process in memory and perform SQL push down (leveraging Snowflake warehouse for processing). Push down technique is excellent help on Big Data use cases, as it eliminates the bottleneck with the tool's memory.

#4 Managing Snowflake

Here are a few things to know after Snowflake is up and running: Security practices. Establish strong security practices for your organization—leverage Snowflake role-based access control (RBAC) over Discretionary Access Control (DAC). Snowflake also supports SSO and federated authentication, merging with third-party services such as Active Directory and Oakta.

Access management. Identify user groups, privileges, and needed roles to define a hierarchical structure for your applications and users.

Resource monitors. Snowflake offers infinitely compute and scalable storage. The tradeoff is that organizations must establish monitoring and control protocols to keep your operating budget under control. The two primary comes here is:

Snowflake Cloud Data Warehouse configuration. It's typically best to curate different Snowflake Warehouses for each user, business area, group, or application. This assists to manage billing and chargeback when required. To further govern, assign roles specific to Warehouse actions (monitor, access/ update / create) so that only designed users can alter or develop the warehouse.

Billing alerts assist with monitoring and making the right actions at the right time. Define Resource Monitors to assist monitor your cost and avoid billing overage. You can customize these alerts and activities based on disparate threshold scenarios. Actions range from suspending a warehouse to simple email warnings.

Final Thoughts

If you have an IoT solutions database or a diverse data ecosystem, you will need a cloud-based data warehouse that gives scalability, ease of use, and infinite expansion. And you will require a data integration solution that is optimized for cloud operation. Using Stitch to extract and load data makes migration simple, and users can run transformations on data stored within Snowflake.

As a Snowflake Partner, we help organizations assess their data management requirements & quantify their storage needs. If you have an on-premise DW, our data & cloud experts help you migrate without any downtime or loss of data or logic. Further, our snowflake solutions enables data analysis & visualization for quick decision-making to maximize the returns on your investment.

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BI Consulting Services

For every Business Intelligence implementation, a proper Roadmap and strategy are essential. But companies sometimes lose a track of the vision to achieve the final step, this results in lower adoption rates leading to lower ROI.

But with Polestar Solutions’ detailed BI consulting Roadmap we provide our clients with BI Strategy And Roadmap, Project Planning, Master Data Management, Big Data Integration, and Support in choosing the right Business Intelligence Solution suited to their needs.

Schedule a free demo with us today, if you want to know more

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Empowering Business Decisions With Actionable Insights & Self-Service BI Dashboards & Reports

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If you are curious about all the possibilities that Anaplan can offer to ensure effective workforce planning, book a session with our Anaplan Experts at Polestar Solutions.

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Integrating Data With Anaplan and Extending Its Functionality

3 keys to Anaplan extensibility

It may be difficult to construct informed plans with the most current information if enterprise data is dispersed across functions and integrated through disparate tools. Business leaders and stakeholders have a difficult time accessing and incorporating meaningful and relevant data because of disconnected systems and data. Teams from different departments need to work together to use a single source of truth and to find common ground by pulling in data from a variety of third-party integrations. 

Using Anaplan Data Integration options, you can easily integrate data with built-in extensions and third-party extensions, providing access to enterprise-wide data on a single platform. Connecting through an open platform enables teams to share information easily, allowing stakeholders to get deeper insights and generate more impactful business plans. Organizations that use Anaplan can make better decisions through its extensibility and interoperability options.

Extending an enterprise system creates interoperability with other enterprise systems. An extensible platform allows for data integration and exchange with third-party sources and systems. Using a three-pronged approach, Anaplan enables users to both bring in data for planning purposes as well as extract data to external systems:

• Connectors

• Integrations

• The use of web scripts, tools, and APIs

This three-pronged approach allows enterprises to aggregate data from different systems, execute plans based on a single source of truth, and extend the reach of tools that lead the way. Anaplan's robust ecosystem of partners and purpose-built integrations deliver what the company promises: true interoperability.

Fast and easy connections

With Anaplan connectors, you can automate imports and exports of Anaplan data using third-party services that offer simplified user interfaces, allowing you to import and export data using third-party services. Data stakeholders can easily access basic self-service options and fast access to the data they need. With simplified self-service capabilities, business leaders can quickly access the information they need to answer planning questions without having to rely heavily on IT, thereby accelerating integrations and time to value. Various connectors are available for extract, transform, load (ETL) and enterprise service bus (ESB) tools from Anaplan.

Integrations: Interoperable and intuitive

By integrating Anaplan with multiple sources, users can combine data from on-premises, SaaS, or the cloud into one platform. To create more informed plans, larger data sets can be used. In addition to simple and intuitive configuration and use, these integrations democratize data access to business users, accelerating business decisions.

Anaplan HyperConnect

With Anaplan HyperConnect powered by Informatica, both on-premises and cloud applications can be integrated with Anaplan using the technology of Informatica Cloud. Through HyperConnect, you can easily integrate data from any source into your enterprise planning system. The Informatica Cloud enables business users to connect and integrate data to and from Anaplan at a fraction of the cost. Through a single point of contact, IT involvement is reduced to get started with this integration solution. 

Anaplan CloudWorks

Anaplan CloudWorks enables more intelligent and agile planning. In addition to integrating with external sources such as cloud-based data and services, the tool integrates with internal sources too, such as Anaplan models. Users can easily manage, configure, and run integrations thanks to its intuitive application user interface. Additionally, CloudWorks provides the ability to automate imports from one model to another, trigger-based event processing, workflows, processes, and import/export integrations, and will extend to seamless integrations with third-party AI services in the future. Users of Anaplan's CloudWorks interface can integrate data from Amazon S3, Microsoft Azure Blob, and Google BigQuery.

Toolkits, scripts, and APIs: Efficient and extensible

As part of Anaplan, there are also other options for integration, including a web UI, Anaplan Connect, and APIs, which can be used to extend an organization's Anaplan capability and gain insight into data. By providing easy access to data, these services drive further efficiencies throughout the organization.

Using web tools

Anaplan offers users the ability to import and export flat files directly from the user interface. To quickly extract and share files, teams can use web tools in either the classic UX or the New UX. Several file formats can be imported and exported from Web tools, including .txt, .csv, and .xlsx.

Anaplan Connect

The Anaplan Connect utility automates imports and exports using integration scripts for intranets. On a dedicated computer or server behind your network firewall, users can create integration scripts and execute them. The users of Anaplan Connect can schedule scripts to run on different operating systems as well as third-party schedulers.

Anaplan APIs

By supporting Anaplan Connect, ETL connectors, and custom integrations, Anaplan REST APIs enable better visibility into actions performed. Users can also view more information about their workspaces and models using REST APIs, in addition to supporting data imports, exports, deletions, and file downloads. Anaplan provides users with the ability to configure import and export actions and access public APIs.

The new set of Transactional APIs by Anaplan enable seamless integration and help teams quickly implement requirements. Transactional APIs are easy to configure, requiring minimal preparation and technical expertise, so users can extend Anaplan capabilities through more comprehensive integrations, gaining deeper insights into Anaplan data. Enhanced usage monitoring, reporting, and analytics provide users with a better understanding of workspaces and models. They offer deeper visibility into the models, and multiple file formats, such as CSV and JSON, enabling teams to discover new use cases.

Anaplan Transactional APIs

Both model builders and administrators are interested in automated and scripted methods for determining workspace and model usage for expanding the use cases, but this can sometimes be time consuming. Through Transactional APIs, teams can quickly understand how users are interacting with their workspaces. You can find out how many models you have, how big they are, and how much space each model takes up. The ability to respond quickly to requests and speed up decision-making is enhanced by these tools. 

Summary

The wide range of Anaplan's extensibility and data integration options help enterprises integrate Connected Planning into every aspect of their business. Through their open platform and APIs, Anaplan connectors, integrations, and APIs not only enable more stakeholders to have access to critical data, but also democratize that access. With the help of flexible integrations, teams can share data across multiple departments, allowing them to create more informed plans and forecasts.

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We bring years of expertise in implementing analytics solutions suited to custom enterprise needs. Our solutions assists enterprises develop conclusive fact-based strategies, empowering users and delivering a competitive edge. Talk to our experts today!

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At Polestar Solutions, our innovative Retail analytics solutions exceed industry standards and ensure customer attraction, acquisition, and retention. Our analytics and custom applications deliver a streamlined and rich omnichannel experience. Book a session with us today!

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Keep reading if you want to know more about Anaplan’s Extensibility and Integrations, their types, and how to implement them.

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Turning Data Into Value With Analytics

According to Gartner, “through 2022, only 20% of analytic insights will deliver business outcomes.” That means that organizations are not receiving the returns on the huge investments they have made towards Analytics. But data and analytics have permeated through organizations and individuals at some or the other level, from analytics at a small level to Advanced Analytics with forecasting techniques. So, finding out the necessary analytics and finding what would be pertinent to your business is a necessary skill.

In this blog, we’ll be talking about the Advanced Analytics space or as it otherwise called the Data Science space. Before we go any further, a brief introduction to what we mean by Advanced Analytics.

The types of analytics have evolved from 4 types to 5 types, let’s talk about the 5 of them namely:

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

Cognitive Analytics

With Advanced Analytics, we mean Predictive, Prescriptive, and Cognitive Analytics. Predictive Analytics deals with the prediction and forecasting of future events & Prescriptive Analytics provides insights on what a business should do to solve a problem.

Cognitive Analytics is the most advanced form of analytics that combines a number of intelligent technologies like artificial intelligence, machine learning algorithms, deep learning models, etc. to processes the information and derive results to match human thinking (or understand Human thinking and language)

Analyzing data to identify business opportunities

According to an article by HBR, identifying two things are important. One, the Key Business Questions i.e. the business problems that you are trying to solve. And two, the capabilities you have to solve the resources. Most companies are stuck in solving the High-Value Key Business Questions (KBQ), and especially with the ones having that we have the low capability to solve, which are termed as pipeline dreams. It is important to prioritize High-Value Key Business Questions (KBQs) Over Pipe Dreams.

And many organizations start focusing on short-term trends and are less proficient at predicting obstacles in the future. With Advanced Analytics Solutions transforming businesses in every industry and every domain in organizations, a clear picture can be formed by the organization. Let’s look at some instances where prediction and forecasting are helping organizations.

Using Analytics to predict Maintenance schedule

Real-time and IoT data from sensors can be pulled and analyzed to understand the pain areas and help in improving machine efficiency. This combined with the past data can be useful for generating insights.

Fraud Detection with Graph Analytics

By using Graph Neural Networks (GNNs) models and combining it with Machine learning algorithms, can help improve the performance and interpretability of business problems. This can be useful for the detection of fraudulent financial transactions in banks and even in the health care sector to identify fake claims like health care providers, physicians, and beneficiaries acting together.

Improve Internal Processes with Data

Through data analysis, business drivers can get a clearer view of what they are doing efficiently and inefficiently within their organizations. Data mining will help you generate deep insights for your organization. For example, in manufacturing, you can analyze the amount of defectives pieces or defects being generated to trace them back to the machine or the process causing them. Also by analyzing the working parameters, we can optimize them to give a better throughput yield. Such a deep analysis can help manufacturing processes achieve Six sigma quality and increase profitability from existing processes.

Churn Prediction for Human Resources

Applications of AI and machine learning are transforming & improving the hiring process in many organizations, like having one-stop recruiting solutions. Other applications of data analytics in people management include churn prediction in organizations, having an early warning system, and take proactive steps to increase retention.

Lean Supply Chain Management

Focus in manufacturing and automotive industries has been moving towards lean supply chain management. Demand forecasting and capacity planning help organizations have an optimum capacity of production, which can be collaborated with Store and Distribution data to decrease the inventory in the warehouse and also avoid stockouts. Also, analytics can be helpful to maintain optimized inventory by analyzing the Lead time from both upstream and downstream activities. Strategic risk management with Vendor Evaluation can help firms reduce fraud and sunk costs.

NLP &AI-Powered chatbots

Natural Language processing is being implemented everywhere right from Business Intelligence tools like Power BI and Tableau to Advanced implementation like Chat-bots. Don’t just think of the chatbots used in Websites, but also AI-powered bots like Chaplin AI to bring Analytics to your fingertips, by analyzing the queries and data in the human language without requiring much analysis.

The implementation of Advanced Analytics services is not limited to only these, there are many more solutions like Sentiment Analysis, Fraud Detection, Supervised and Unsupervised learning, Cluster Analysis, and also cross and up-selling. Advanced analytics can initially be something of a juggling act, but once all the pieces start to fall into place, the returns can be game-changing.