Recent Posts

Data Management

OLAP tools, Online Analytical Processing tools

The Online Analytical Data Processing: a Review

Are you using all the data you have? Do you have that data integrated and ready for integrated analysis? Are you using analytics tools on your data?

We have more than two decades of availability of technology for data analysis. We went to the business intelligence space from reporting, where it was not a good idea to run all large data queries in the operational systems. By that time, it was started to talk about online analytical processing tools (OLAP tools), when hardware and software were not as powerful as of today. And during this period, the amount of data generation has not stopped increasing, and it will continue.

Many companies had a journey using these technologies and migrating to the latest ones. However, today, when we are talking about data as the new source of energy, the new oil or the new gold, are all companies harnessing the power of data? 

Even if you are a medium or small business, you cannot wait to unlock the power of the data that you have in your company and the possibilities future and aggregated data will have. If you don’t do it right now, your competitors will be taking advantage.

Let’s review from where we are coming to position yourself in that journey and take action on the data you are not harnessing yet. OLAP or Online Analytical Processing is a concept where you can produce a multidimensional analysis of your business data. It also can provide you the capability for complex calculations, data modeling, and trend analysis.

Online Analytical Processing tools give the insight and understanding that end-users need for better decision-making by performing specific analyses of multidimensional data interactively from multiple perspectives. 

OLAP tools involve relational databases, report writing, data mining. Online Analytical Processing tools performed on a Cube consist of five basic analytical operations:

  1. Drill down
  2. Roll up
  3. Dice
  4. Slice
  5. Pivot

Online Analytical Processing tools are the basis for many business applications for Business Performance Management, Planning, Analysis, Budgeting, Simulation Models, and Data Warehouse Reporting. 

OLAP databases allow for complex analytical and specific queries with a fast execution time using a multidimensional data model. An OLAP system consists of an OLAP cube, which is also known as a multidimensional cube or a hypercube.

Main Online Analytical Processing tools are classified as: 

  • MOLAP: Multidimensional Online Analytical Processing tools
  • ROLAP: Relational Online Analytical Processing tools
  • HOLAP: Hybrid Online Analytical Processing tools

Why OLAP Tools?

As knowledge is the foundation of all successful decisions, businesses need to continuously plan, analyze, and report sales and operational activities to maximize efficiency and become successful businesses. 

An important thing to do to improve your business activity is to collect as much data as possible. As businesses collect data using many different systems, you have the challenge to get all the data together to create reliable and accurate information about your business. 

Your business will be better positioned to make better decisions and win the competition if you turn data into shared knowledge quickly and accurately.

OLAP tools technology can be defined as the capacity to deliver “fast access to shared multidimensional information.” Thereby, OLAP tools technology can help you make better and quicker decisions based on reliable and accurate information.

Benefits of OLAP Multidimensional Databases

The main difference between Relational Databases and Online Analytical Processing tools is that OLAP tools do not store individual transaction records in a row-by-column format like a worksheet.

Online Analytical Processing tools use some multidimensional database structures called Cubes to store arrays of consolidated information. The data is stored in an optimized multidimensional database.

Your business is a multidimensional activity that needs to track its actions by considering many variables. If you follow these variables on a spreadsheet, you will use X and Y axes where each axis represents a group of logical variables or categories.

You can track your sales in units in time using these types of schemes—for example, sealed units in rows and months in columns. Sometimes, a business needs to track many variable groups or parameters, which may be beyond the scope of any number of linked spreadsheets.

In an Online Analytical Processing (OLAP) environment, these variable groups or parameters are called Dimensions. Instead of storing individual transaction records in a two-dimensional format  (row-by-column) like a worksheet, OLAP tools use Cubes that are multidimensional database structures.

Users can view a Cube on demand and produce a worksheet-like view of the data stored in the optimized multidimensional database.

Companies have many dimensions to track. A business that distributes goods from several facilities will have many Dimensions to consider as Products, Accounts, Locations, periods, Salespeople, among others. 

These Dimensions represent the “whole” business knowledge, providing the basis for all business activities (planning, analysis, and reporting).

Companies need OLAP technology to own the capability to perform multidimensional analysis so that users can view and manipulate data along the multiple dimensions required.

How do OLAP Tools work?

Online Analytical Processing tools are used to extract or retrieve data to be analyzed from different viewpoints. These tools work by extracting data from multiple sources and various formats and storing them in Data warehouses. After that, the extracted data is cleaned and transformed. 

Data is stored in OLAP Cubes, where information is pre-calculated in advance for further analysis.

Then users can get the information they need by running queries. 

The OLAP Cube consists of numerical facts called measures which are categorized by dimensions. The Cube can store and analyze multidimensional data in a logical and orderly manner.

Classification of OLAP Tools

There are mainly two types of Online Analytical Processing tools: Multidimensional OLAP (MOLAP) and Relational OLAP (ROLAP). Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP and ROLAP.

MOLAP

MOLAP or Multidimensional Online Analytical Processing uses a multidimensional cube that accesses stored data through various combinations. 

MOLAP products use a “Multi-cube” approach where a series of pre-calculated cubes make up a hypercube. Data is first summarized and then stored in contrast to ROLAP, where queries are served on-demand. 

It is easy to use even for inexperienced users thanks to its simple interface. It turns out to be the best tool for “slicing and dicing” operations due to its speedy data retrieval.

MOLAP is the more traditional way of OLAP analysis. It has excellent performance and can perform complex calculations. The downside is that MOLAP can only handle a limited amount of data and is less scalable than ROLAP.

Among the most known products that emerged running off MOLAP:

  • Oracle Essbase
  • IBM Cognos
  • Apache Kylin

ROLAP

ROLAP or Relational Online Analytical Processing stores data in relational tables using columns and rows. It retrieves the information on demand through user queries. 

A ROLAP database can be accessed to calculate information by complex SQL queries. Besides, it can handle large data volumes, but the more data it processes, the slower the processing time. 

It does not require the storage and pre-computation of information because queries are made on-demand. 

ROLAP relies on manipulating the relational database’s data to give the appearance of traditional OLAP’s slicing and dicing functionality. Each action of slicing and dicing is like adding a “WHERE” clause in the SQL statement.

ROLAP can handle large amounts of data, and it doesn’t impose limitations on data amounts.

Besides, it can leverage functionalities inherent in the relational database. The downside with ROLAP is that it has potential performance constraints and scalability limitations.

Examples of popular ROLAP products included Metacube by Stanford Technology Group, Red Brick Warehouse by Red Brick Systems, AXSYS Suite by Information Advantage, several of them acquired by IBM and incorporated into the business intelligence data suites.

HOLAP

HOLAP or Hybrid Online Analytical Processing connects attributes of both MOLAP and ROLAP. 

These technologies try to combine the advantages of MOLAP and ROLAP. 

Users get the benefits of both since HOLAP involves storing part of data in a ROLAP store and another part in a MOLAP store. With this use of the two OLAP tools, the data is stored in relational databases and multidimensional databases.

The choice to access one of the databases depends on which is most appropriate for the requested processing application. This hybrid approach provides much more flexibility for handling data. 

The data is stored in a relational database for heavy processing and in a multidimensional database for theoretical processing. 

Some products emerged running HOLAP with Microsoft Analysis Services and SAP BI Accelerator. The Analysis Services technology is now available in Azure, Power BI, and on-premises with SQL Server. 

The SAP BI Accelerator product went out to the free market, and all functionalities have been incorporated and very much improved into the new SAP HANA InMemory Database technology. 

What Are OLAP Cubes?

An OLAP Cube is a data structure that enables fast data analysis according to the multiple Dimensions that define a business problem.  

The multidimensional cube for reporting services sales of an IT Business might be composed of many Dimensions like Salesperson, Amount, Region, Services, Month, Year, among others.

Cube is a shorthand for a “multidimensional dataset” where data can have an uncertain number of dimensions. A cube is not a “cube” in the strict mathematical sense. But this term is used widely, although all the sides are not necessarily equal.

The term cube here refers to a multi-dimensional dataset, which is sometimes called a hypercube if the number of dimensions is higher than three.

A determined number of cells forms a cube. Every cell of the cube holds a number representing some measure of the business, such as profits, expenses, budget, or sales.

Another essential term when we talk about Online Analytical Processing tools and Cubes is a “Slice.” It is a term for a subset of the data generated by picking a value for one dimension and only showing the data for that value. 

OLAP Cube Advantages

The organization of data into Cubes appears due to the limitations of relational databases that were not considered for the analysis and display large amounts of data. 

OLAP Cubes are better suited than relational databases when a whole database must be summarized. They are the best option when users wish to re-orient reports or analyses according to different, multidimensional perspectives called Slices. 

The use of OLAP Cubes facilitates a fast end-user interaction with data. It can be thought of as an extension of a spreadsheet’s modeling structure (that only accommodates data in rows and columns).

OLAP Cubes’ designers can build models that balance user needs and logical model limitations due to Cubes’ capacity to include many arrays or Dimensions.

OLAP Tools Analytical Operations

(Images: https://www.geeksforgeeks.org/olap-operations-in-dbms)

OLAP is a business intelligence unit (BI) that answers multi-dimensional analytical queries more smoothly and faster. 

Online Analytical Processing tools hold the relational database and encompass RDBMS and data mining & reporting. OLAP tools give users the capacity to analyze multidimensional data from multiple perspectives.

Five basic analytical operations can be performed on an OLAP cube:

  1. Drill down
  2. Roll up
  3. Dice
  4. Slice
  5. Pivot

#1. Drill down

The less detailed data is converted into highly detailed data in drill-down operations. Two different kinds of functions can do it:

  • Moving down in the concept hierarchy
  • Adding a new dimension

The example below shows the drill-down operation by moving down in the Time dimension concept hierarchy (Quarter -> Month).

Images: https://www.geeksforgeeks.org/olap-operations-in-dbms/

#2. Roll up

A Roll up operation is the opposite of the drill-down operation. It performs aggregation on the OLAP cube. 

It can be done by:

  • Climbing up in the concept hierarchy
  • Reducing the dimensions

The cube below shows the roll-up operation performed by climbing up in the Location dimension concept hierarchy (City -> Country).

#3. Dice

The Dice operation selects a sub-cube from the OLAP cube by selecting two or more dimensions. 

The cube below shows a sub-cube selection following dimensions with this criteria:

  • Location = “Delhi” or “Kolkata”
  • Time = “Q1” or “Q2”
  • Item = “Car” or “Bus”

#4. Slice

The Slice operation selects a single dimension from the OLAP cube, which results in a new sub-cube creation. 

The cube below shows how a Slice operation is done on the dimension Time = “Q1”.

#5. Pivot

Pivot, also called rotation operation, rotates the current view to get a new representation view. 

In the sub-cube obtained after the slice operation, performing pivot operation gives a new view of it.

Some OLAP Tools from where we are coming

SAP NetWeaver Business Warehouse

The SAP NetWeaver Business Warehouse (SAP NetWeaver BW) is one of the most known and used OLAP tools. It has the purpose of reporting, analysis, and interpretation of business data. 

These actions are essential to enhance companies’ competitiveness by optimizing processes and enabling them to react instantly and in line with market needs. 

SAP NetWeaver BW provides tools and functions that facilitate companies to achieve these goals. If you get SAP NetWeaver BW, you can integrate and consolidate appropriate business information from productive SAP applications and external data sources. 

This group of tools also gives you a high-performance infrastructure that helps you evaluate and interpret data. Due to this, you can make well-founded decisions and identify target-orientated activities based on the analyzed data.

Integration with In-Memory Technologies

One key point of this group of SOAP tools is that you can enhance SAP NetWeaver BW’s performance using in-memory technologies.

This feature allowed you to efficiently process demanding scenarios with high data volumes, unpredictable query types, high query frequency, and complicated calculations.

You can use a database to access the data from a BW object faster by storing it as an index in SAP NetWeaver Business Warehouse Accelerator. 

SAP HANA database for data persistence offers further benefits when executing analysis and planning scenarios besides the performance benefits. 

IBM Cognos

IBM Cognos is an integrated and analytical web-based processing system from IBM. It contains numerous inbuilt components to meet various information requirements in an organization. Due to its toolkit, it can also perform analysis for reporting and score carding and the provision to monitor metrics.

IBM Cognos id formed by several windows based components named as IBM Cognos Framework Manager; cube designer; IBM Cognos Transformer; map manager; IBM Cognos Connection; and IBM Cognos Report Studio.

For instance, IBM Cognos Report Studio is used to create reports that are shared with knowledge processing departments. This element allows creating any type of report, including charts, lists, maps, and repeat function.

Analysis Studio is also used to search for background information about an action/ event and prepare the analysis of large data sources. It includes some essential OLAP tools like roll up and drills down. Those are used to get a better understanding of information.

Cognos Analytics Features

Cognos allows you to perform automated data preparation, data discovery, and visualizations to make more confident decisions. To achieve it, you need to complete the following steps:

  • Get connected: You need to connect your data effortlessly from your CSV files and spreadsheets, from the cloud or on-premises data sources, including SQL databases, Google BigQuery, Amazon, or Redshift.
  • ‎‏‏‎Prepare your data: Then, you can prepare and connect your data automatically with AI-assisted data preparation. Clean and prep data from multiple sources, add calculated fields, join data, and create new tables.
  • Build visualizations: Cognos allows you to create dynamic dashboards easily. Drag and drop data to create auto-generated visualizations, drill down for more detail, and share using email or Slack.
  • Identify patterns: You can ask the AI assistant a question in plain language and see the answer in a visualization. Use time-series modeling to predict seasonal trends.
  • Generate personalized reports: You can also keep your stakeholders up-to-date, automatically. Create and share dynamic, personalized, multi-page reports in the formats your stakeholders want.
  • Gain insights: Cognos lets you make confident data decisions. Validate what you know, identify what you don’t with statistically accurate time-series forecasting, and pinpoint patterns to consider. 
  • Stay connected: You can stay connected 24/7 on the go with the new mobile app. Access data and get alerts will always be right from your phone.

Micro Strategy

MicroStrategy is a Washington-based enterprise business intelligence (BI) application software vendor that provides services on BI and mobile software worldwide. 

The MicroStrategy platform supports interactive dashboards, scorecards, highly formatted reports, ad hoc queries, thresholds and alerts, and automated report distribution. 

MicroStrategy Analytics allows companies/organizations to analyze large volumes of data and distribute business-specific insight.

It delivers reports and dashboards to the users and allows them to conduct and share analysis via mobile devices. It is a secure and scalable software with excellent governance features of enterprise-level BI.

MicroStrategy’s platform uses single standard metadata for consistency and streamlined maintenance. MicroStrategy’s 64-bit architecture supports in-memory analytics with “Intelligent Cubes” that are OLAP reports cached in memory as data sets. 

Metrics and attributes are created once and used across different types of reports. When a change is made in one place, all related reports are automatically updated. In the same way, security permissions are given in only one place to reduce administration costs.

MicroStrategy helps make more reliable decisions and build a more innovative enterprise. It is available on-premises software and host-based service in MicroStrategy Cloud. 

PowerBI

Power BI is a Microsoft business analytics service that provides interactive visualizations and business intelligence capabilities. It features an interface simple enough for end-users to create their dashboards and reports. 

Power BI is formed by a collection of apps, software services, and connectors that work together to turn your business’ unrelated sources of data into logical, visually immersive, and interactive insights. 

Power BI can get data from a single Excel spreadsheet or collect cloud-based and on-premises hybrid data warehouses. It allows you to easily connect to your data sources, visualize and discover what’s important.

This application was conceived originally by Thierry D’Hers and Amir Netz of the SQL Server Reporting Services Team at Microsoft. After several evolutions, it was renamed as “Power BI,” and Microsoft then revealed it in September 2013 as Power BI for Office 365.

With PowerBI, you can visualize and connect to any data using the unified, scalable platform for enterprise business intelligence that’s easy to use and helps you gain deeper data insight.

About ZirconTech:
A trusted partner that helps organizations thrive in their digital transformations. Mobility, Internet of things, artificial intelligence, big data, cloud computing and blockchain technologies.

Data Integration Benefits. People working to integrate data.

What is Data Integration?

Thanks to Data integration, your company can use data collected from different systems in a unified way. So the unified data transforms into a more valuable asset for your business. Among all the data integration benefits, one of the main ones is helping people inside the company work better and do more for your customers. But when a company doesn’t use data integration benefits, people in the company don’t have the means of obtaining the data from the business’s different unintegrated systems. 

Clients may receive emails from different areas of the company requesting information that the clients have already delivered. But other areas of the company cannot access the same information because the information systems aren’t integrated. Sometimes unintegrated systems lead to data being shared manually via spreadsheets or emails, which increases the probability of mistakes in the future.

Another of the leading data integration benefits is helping to avoid this kind of problem in modern companies. In this way, information can be shared between ERP or CRM systems and vice-versa. This way, everyone in your company has the needed data to work properly.  Mistakes are eliminated, and the whole business gets the benefit out of the available systems. 

YOU CAN BE INTERESTED IN THESE ARTICLES TOO:
1. Data Lifecycle Management (DML) Best Practices
2. Data management: benefits and keypoints
3. What are OLAP (online analytical processing) tools?

A Short Story of Data Integration

In the past, communicating information within a company, such as purchase orders and invoices, was made traditionally on paper. There were isolated systems in a single company that can produce some batch processing that could send data from one to another through an electromagnetic device or just paper. 

Although Electronic data interchange (EDI) was created in the early 70s, the use within and between companies was not very spread.Technical standards like EDI appeared to facilitate businesses communicating information electronically. EDI replaced paper and used electronic means to share information within companies.

Word of 1990: Data integration benefits

In the middle of the 1990s, the National Institute of Standards and Technology defined Electronic data interchange as the computer-to-computer interchange of strictly formatted messages representing documents. In those years, the different systems inside a company weren’t integrated.

So the 1990s were about breaking down internal walls. Companies were confronting the challenge of integrating and redesigning enterprise processes using ERP (Enterprise Resource Planning) systems. But at that time, enterprises transacted with each other via mail, phone, and fax. These interfaces were cumbersome, slow, labor-intensive, costly, and prone to generating mistakes that caused delay, error, overhead costs, and limited communication. 

With the advent of the internet, more and more organizations were connected. Finally, most or all EDI communications were moved to the Internet. The connection was the solution and the internet-enabled high-quality fast connections with the right amount of data.

World of 2000: Data Integration Benefits

The 2000s were about breaking down external walls by integrating and redesigning inter-enterprise processes using the Internet. A fast, cheap, and ubiquitous communication system. A tool for changing how companies worked together. A catalyst for reengineering inter-enterprise processes. Virtual integration enabled multiple enterprises to work as though they were one through processes that ignored enterprise boundaries.

Different Problems of Unintegrated Data

Independent databases

Enterprises that work together nonetheless use separate databases for operations and decision-making. These databases are separately maintained and therefore inconsistent, and they each reflect only part of the overall situation. 

The consequences of this kind of practice are:

  • Sub-optimization
  • Slow cycle times
  • Reconciliation costs. 

The solution to this problem is Database Coordination. Here we find another principal data integration benefits when different departments within a company can work collaboratively with shared data.

Innumerable interfaces

Enterprises have many individual and independent interfaces with other enterprises. These transactions are of small scale individually, and collectively they represent a complex system. The consequences are confusion, redundancy costs, and missed leverage opportunities.

The solution for these problems seems to be the combination of multiple interfaces and multiple transactions into one. These practices benefit from reducing transactional overhead, generating consistent interfaces, and economies of virtual scale. These are other kinds of data integration benefits too.

Fragmented Processes

Enterprises are managed as self-contained entities, performing processes that fit entirely within their boundaries. These processes are, in fact, merely fragments of more extensive inter-enterprise methods. The consequences are redundancy, sub-optimization, and unnecessary overhead. The solution for these problems is compression. So, by treating an inter-enterprise process as a single unit, it is best done. 

This practice has different benefits as:

  • Overhead costs are reduced.
  • Improvement of tasks and process performance.
  • Elimination of redundant activities.
  • Strategic focus.

Companies can  improve their performance by:                                    

  1. Streamlining interfaces (connecting)
  2. Sharing information (coordinating)
  3. Aggregating interfaces (combining)
  4. Integrating processes (compressing).

The benefits of these practices can be the elimination of non-value-adding overhead created by enterprise walls and the improvement of their performance by leveraging other enterprises. Putting a Web site in front of lousy processes merely advertises how lousy they are is not the right business solution. Companies need to integrate benefits internally to integrate externally. This way, they are experiencing data integration benefits.

Data Integration Architecture

According to Philip Russom’s article today, many data integration specialists still build one independent interface at a time, which is inherently anti-architectural. And a common misconception is that using a vendor product for data integration automatically assures architecture.

But if you don’t fully embrace data integration architecture, you can’t experience data integration benefits. You will not get to know how architecture affects data integration’s scalability and ability to support real-time, master data management, and interoperability with related integration and quality tools. 

Complexity is the main reason why data integration needs architecture. Data integration affects data flow from diverse source systems like operational applications for ERP, CRM, and supply chain, where most enterprise data originates. Through multiple transformations of the data to get it ready for loading into diverse target systems like data warehouses, customer data hubs, and product catalogs. 

Since these are various types of applications, database brands, file types, and so on, all these have different data models, so the data must be transformed in the middle of the process. Then there are the interfaces that connect these equally diverse pieces. And the data doesn’t flow uninterrupted, or in a straight line, so you need data staging areas. Simply put, that’s a ton of complex and diverse stuff that you have to organize into a data integration solution to profit from data integration benefits. 

That is why nearshoring a data integration partner will ease the process. Not all vendors are qualified; we suggest this article to find yours.

Goals of Data Integration Architecture:

Data integration architecture imposes order on the chaos of complexity. It makes companies serve from data integration benefits by achieving specific goals:

1) Architectural patterns as development standards: Most components of a data integration solution fall into three broad categories: servers, interfaces, and data transformations. With that in mind, we can think that Data integration architecture is simply the pattern made when servers relate through interfaces. An architectural pattern is to provide a holistic view of both infrastructure and the implementations built on it. 

2) Simplicity for reuse and consistency: When development standards and architectural patterns are applied to multiple data integration projects, the result is simplicity, which promotes the reuse of data integration development artifacts and increases consistency in handling data.

3) Harmony between common infrastructure and individual solutions: For a solution to be organized in preferred architecture, the infrastructure must enable that architecture. Mostly the data integration production server and the interfaces it supports. 

The most common architectural pattern for data integration is Hub-And-Spoke architecture. In this architecture, inter-server communication and data transfer pass through a central hub, where an integration server manages communications and performs data transformations. 

What is the GDPR? 

The General Data Protection Regulation (GDPR) is the most robust privacy and security law in the world that was put into effect on May 25, 2018. This Europe’s data privacy and security law include a significant number of requirements for organizations around the world. 

It imposes obligations onto organizations anywhere, so long as they target or collect data related to the EU people. The GDPR has the power to impose harsh fines against those who violate its privacy and security standards, with penalties reaching into the tens of millions of euros.

Thanks to the GDPR, Europe signifies its firm stance on data privacy and security when more people are entrusting their data with cloud services, and breaches are a daily occurrence. The regulation itself is extensive, far-reaching, and relatively light on specifics, making GDPR compliance a daunting prospect, particularly for small and medium-sized enterprises (SMEs).

It’s worth mentioning GDPR in this context because when you harness the power of data, integrate all your internal databases, and enrich your systems with aggregated and external data, you have to look at the regulations and be compliant.

What is DataOps?

In this article, Andy Palmer tells us that large companies are experiencing a foundational shift in how they view and structure their data. 

Nowadays, organizations capture more data than ever before and store it in an ever-increasing variety of data stores. The accumulation of data over time makes companies struggle to manage that tremendous volume of data. 

For decades, companies’ data environments have been deeply fragmented and virtually impossible to integrate at scale. Indeed, even basic questions about the business, like “Who are my customers?” can’t be answered consistently. This reality is making companies accept that they need to start managing their data as an asset, and It’s time to rethink priorities and start putting the “data horse in front of the analytics cart.”

Data Operations (DataOps) is a methodology for companies where people process data from different business data sources quickly, regularly, and reliable. The concept is that the best way to increase the velocity of new features being delivered in software is by using a continuous build, test, and release process with a strong emphasis on QA automation. 

Companies expecting to compete based on analytics need to empower analysts with easy access to updated unified data logically organized. By implementing a data curation process that is integrated, repeatable, and scalable, it will be possible for a business to serve from data integration benefits, achieving the analytic velocity necessary to create a competitive advantage.

Data Integration Benefits 

According to this article from youredi.com, Cloud-based integration platforms have started to become quite popular lately. Below you can read about seven main Data Integration Benefits that companies can see once they have switched to cloud-based integration platforms: 

1) Easy and fast connections: Developing connections have been a painful task that took a long time, even months sometimes. Point-to-point hand-coded integrations are time-consuming and risky. When you need to integrate data sources internally, you want to ensure that it happens as quickly as possible. Doing data integrations in the cloud seems to be the best new way. It is easy and fast because of the pre-built adapters and connectors that can be easily replicated.

2) Integrate data from multiple sources: When your company uses tons of applications, systems, and data warehouses. Better collaboration must connect all the different data sources to utilize the value of insights. Once all the information is available in a single place in real-time for all the right stakeholders, your company will be able to use the information for improving processes and providing better services.

3) Availability of the data: As Data Silos are not sustainable, companies need to have the data available for all the right stakeholders in one place and in real-time. So, it’s essential to connect all the data sources to obtain all the necessary information in a single place.

4) More insights bring improvements: Once your company has all the data available in one place, you will be able to better utilize the available information. When you can use the data, you will have better intelligence on your operations and customers. You will be able to make better decisions based on the available information and improve your processes.

5) Better collaboration: When your company needs to improve the cooperation internally or with your trading partners, you will do so thanks to the Data Integration Benefits. By automating the flow of information will have a positive impact on how you do business. Your teams and stakeholders will achieve better collaboration because they have more information at their disposal.

6) Data integrity and data quality: Data integrity is an essential element of data integrations. Data integrity is the assurance of the consistency and quality of the data through its entire lifecycle. Using a data integration platform can give your business the ability to define validation rules. You can design processes that check the information and forward the damaged information back to the sender for correction.

7) Increase competitiveness: Having a data integration strategy for your company can help you plan what actions you need to take to improve data accessibility both externally and internally. This way, your company will be able to impact a lot of vital parts of your business. 

Finally, we can say that thanks to these seven Data Integration Benefits, people inside your company will be able to:

  • Work better among them and do more for your customers.
  • Use data collected in a unified way as a valuable asset for your business. 
  • Offer better services to your customers than your competitors.
Data Management Lifecycle Best Practices. People analyzing data.

Pandemic and isolation during 2020 have left us many lessons. Of course, it was a challenging time, full of limitations, uncertainty, and new challenges. But also full of valuable opportunities in the personal, work, and business fields. We don’t know how much time the pandemic will last, but there is a light in the darkness. It’s the certainty that the world has changed because people’s behavior has changed, and also, IT companies have changed forever. 

The operation, objectives, and challenges of this sector are going through the most significant change in the last twenty years. And most of these changes are related to the data management processes. We have never experienced this high level of data collection before. The pandemic and isolation have forced all companies, especially IT companies, to obtain more users’ data. Data that needs to be managed, processed, stored, and protected after being acquired.

Data Is the New Oil

Lately, I read an article entitled Data Is the New Oil of the Digital Economy that talks about how the 21st Century’s digital economy data were like oil in the 19th Century. Although oil was discovered in 1859 when Edwin Drake drilled, produced crude oil, and gave birth to the modern petroleum industry. The oil industry was extensively developed until 1870. When John D. Rockefeller and Henry Flagler with the Standard Oil Co foundation. Standard Oil Co. succeeded in dominating the oil products market thanks to its innovation in the oil industry. This company worked hard optimizing the production, logistics, costs, and business trust at that time. 

YOU CAN BE INTERESTED IN THESE ARTICLES TOO:
1. Data management: benefits and keypoints
2. Data Integration Benefits You Need to Know
3. Gartner’s Top Technology Trends for 2021

We’re undoubtedly in a digital economy where data is more valuable than ever. But, while oil proved to be a limited and harmful asset, data collection can be a true elixir for digital businesses. There will be huge rewards for those who learn how to extract data first—those who first prepare their information systems and start collecting. Here are some interesting facts about the digital data economy in the 21st Century:

  • Digital transformation is accelerating: New disruptive technologies as Virtual and Augmented Reality technology, Robotics, IoT (Internet of Things) are reshaping the way most businesses are functioning today.
  • New technologies such as AI, ML, and the cloud are transforming how IT businesses use data management processes. 
  • 21st century IT companies have the urgency to learn about processes such as Data Management Process, Data Lifecycle Management (DLM), and Information Lifecycle Management (ILM).
  • Data collection and analysis will become by 2030 the basis of all future service offerings and business models. 

What is a Data Management Process?

If data is the new oil, we can say that a Data Management process includes acquiring, storing, protecting, and processing that oil. Suppose you aim to turn your IT business into the next Standard Oil Company. So you need to follow the right process to ensure the accessibility and reliability of your data from users. Think of using Big Data to improve your business decisions and gain deep insights into customer behavior, trends, and opportunities. Also, to create extraordinary customer experiences.

According to this article from NG DATA, you need your company to use data management processes and platforms to make sense of the great quantity of data you need to gather, analyze, and store. Data management processes make some essential functions like processing and validating simpler and less time-consuming. Make use of a data management platform with the needed data management processes. It can allow your business to take advantage of large quantities of data from all data sources in real-time. Besides, it will enable more effective engagement with customers and increases customer lifetime value (CLV). 

It is essential to make use of top data management processes and data management platform because:

  • We are creating and consuming data at unprecedented rates.
  • These platforms give organizations a 360-degree view of their customers.
  • Data management platform allows businesses to gain deep, critical insights into consumer behavior.
  • These platforms give businesses a competitive edge to maintain in time.

Best Practices for Data Management 

An IT business needs to know the questions that need to be answered. So then, acquire the specific data that is necessary to answer those questions. This process will help to get the required insight to make decisions driven by data. To achieve this, IT Businesses need to do the following tasks:

  • Collect vast amounts of information from various sources.
  • Utilize best practices while going through the process of storing and managing the data. 
  • Cleaning and mining the data. 
  • Analyze and visualize the data to execute business decisions.

It’s important to consider that data management best practices result in better analytics that permits companies to optimize their Big Data. IT companies should try these data management best practices:

  • Simplify access to new and emerging data.
  • Debug data to infuse quality into existing business processes.
  • Shape data using flexible manipulation techniques.
  • Use data management platforms to gather, sort, and house their information.
  • Repackage information visualized ways that are useful to marketers. 

It is essential to count on a top-performing data management platform to give your employees the most accurate business information. This what they will be capable of managing all data sources in the best way for the whole organization. It is only through data management best practices that organizations can harness the power of their data to make the data useful. Data management best practices will enable your IT business to use data analytics in beneficial ways, such as:

  • To personalize and add value to your customer experience.
  • To identify the root causes of business issues in real-time.
  • To produce better revenues associated with data-driven processes.
  • To improve customer engagement and loyalty.

What is Data Lifecycle Management (DLM)?

According to this article from Bmc Blog, Data Lifecycle Management (DLM) involves entering a piece of data into a database. This way, employees can access the new data to make reports and analytics. Otherwise, it will remain in the database and eventually become obsolete. Data may have logic applied to different processes, but it will come at some point to the end of its useful life and be archived or purged. The concept of defining and organizing this process into repeatable steps for enterprise organizations is known as Data Lifecycle Management. And it can be made as an automated process to take data through its useful life. 

What is the difference between DLM vs. ILM?

Data lifecycle management is the reactant that pushes data from one stage to the next, from creation to deletion. DLM is all about a system designed to answer an essential question in data life: when should this information be deleted? On the other corner, we have another critical term associated with DLM. Information Lifecycle Management (ILM) seeks to answer another big question: Is this information relevant and accurate?

DLM and ILM are two sides of the same coin. You need to implement a robust system for data management processes or DLM to obtain an effective ILM strategy. DLM and ILM should form part of an organization’s overall data protection strategy. DLM is concerned with the whole mass of data, while ILM deals with what’s in a record. Ensuring that the most recent and useful records are accessible quickly and efficiently is the main target of a good DLM strategy. 

ILM has the valuable task of ensuring that every piece of information included in a record is accurate and up-to-date for the record’s useful life. A record becomes more and more obsolete as it passes through specified lifecycle stages. And when a document becomes obsolete, speed and accessibility are no longer prioritized for stale data.

Best Practices for Data Lifecycle Management  

There are no industry standards for enterprise data lifecycle management. But most experts agree that the different stages of the management cycle look like the ones you can see below:

  1. Data acquisition and capture: These occur at the beginning of the cycle when the organization obtains new information. This data could be in different formats as images or other types of Documents. At this stage, data entry professionals have the critical goal to ensure that the information they receive is as accurate as possible.
  1. Data Backup and Recovery: In the second stage, data entered into the system experiences some archival process that ensures redundancy. Active archives are an ideal storage method for businesses that need to store large volumes of data and have it accessible.
  1. Data Management and Maintenance: These processes make accurate data available in real-time for use and publication. To ensure that the record meets specific validations to be accessible for users. 
  1. Data Retention or Destruction: It is the final stage of the life cycle where data is retained or destroyed. But before destroying any data, it is critical to confirm if any actual policies would require your company to maintain the information for a particular time.

Benefits of Data Management and Data Lifecycle Management

Finally, I want to mention some benefits of these two powerful practices inside IT businesses. It is essential to manage large volumes of data. Your business needs to follow a strategy of Data Management best practices. Through these practices, companies can only control the power of their data and gain the insights they need to make this data useful.

In parallel with Data Management practices, there are also some benefits to why an IT business wants to implement Data Lifecycle Management processes. We can mention: obtaining compliance and governance, protecting valuable data, or making an ILM Strategy. All these benefits help businesses achieve greater agility and efficiency.

About ZirconTech:
A trusted partner that helps organizations thrive in their digital transformations. Mobility, Internet of things, artificial intelligence, big data, cloud computing and blockchain technologies. We can be your nearshore partner, contact us! 

Data Managment Benefits. People analyzing data.

Data is the new energy source. How are you managing data in 2021?

We can say undoubtedly that 2021 is the year when definitely companies have to work with data. Because it is the force that converts knowledge and insights into action. Companies need more than ever to understand its power, to identify the information that they have, and learn to make good use of it with solid practices of data management.

The use of the information has great benefits that companies need to use to achieve an advantage over their competitors. There is one important point that business managers need to have in mind about data management. And this point is that those who use it win and those who don’t suffer the consequences.

What is data management?

Data management depends more on the practices than the technologies that your business has. It combines certain practices like collecting, keeping, and using the information in ways that are secure, efficient, and cost-effective. The connection of different data sources like structured databases or unstructured information makes it possible to obtain the content and knowledge needed to make decisions and take actions that maximize the benefit to the organization. Today, a strong data management strategy is one of the most important practices that a company has to achieve. It’s an intangible asset that creates one of the organization’s major values.

How are you managing data in 2021?

In this modern economy, companies that don’t understand the importance of data management are less likely to survive because a company’s information is its most valuable asset. Data is the foundation of every modern business. Information and knowledge are the main tools to correct decisions and actions. I mean it when I say that data is the new oil. Certainly, information is today’s most powerful energy source for your business. But your company needs help to make effective usage of their data. 

First, you need to know exactly at which point of the data management process your business is. To know it, you first need to answer a series of questions. The industry has been talking about this for at least 5 years, but we are now in 2021, what are we waiting to take advantage of what we have and what we can achieve? Particularly in a pandemic time when the digital transformation only accelerates

Where is your business on the path to effective data usage?

Do you have data?

It seems obvious, but it is the first of all things to know when we talk about data management. Maybe your company needs certain information that it doesn’t have. So first, you have to prepare your company’s information systems to start collecting valuable data.