Data Management – A Historical Perspective

Data Management – A Historical Perspective

It is always interesting to predict the future of technology and data in different ways and from different perspectives to make good decisions. We will therefore define here and present solutions on how Chain-Sys Products and Solutions can fit into an organization’s roadmap. But then prediction is a risky proposition! The best I would try to do is to trace the history of data, applications, and technology over the years since 1959, the year I was born. And I truly believe reading history is the best way to understand the present and predict the future to some extent.

data-management

Computers were built using vacuum tubes serving as switches in the fifties and occupied huge rooms, and cooling the heat from the tubes was a great engineering effort. You would have heard stories of how algorithms were run on such computers to crack the code used by the German Army during the second world war. You would have come across Turing Machines, named after Alan Turing, in your fundamentals of computer science courses. So, in the beginning, computers were used to perform computations and repetitive iterations to arrive at answers. Now we use computers to talk, store photos, do video conferences, play games, etc., activities that are far trivial for the wonders that a computer can actually perform.

Digital Transformation

In the early sixties a very innovative device or switch was discovered in the Bell Labs of the USA. This switch did not have glass enclosures or heated filaments; it was a transistor. This innovation made mainframe computers smaller and available to many organizations. Routine end of day bank postings were done by these machines instead of manual ledger postings. Data in magnetic tapes were crunched and written to other magnetic tapes and to print out voluminous reports. Payrolls were run this way too. In the seventies FORTRAN was the popular language for scientific programs and COBOL was the language for business programs. During this era, airlines were a glitzy and glamorous industry. They put up mainframes and terminals at various agent premises and enabled booking and ticketing. The mainframe also checked in passengers at airports, and allowed gate verification and flight closing.

In the seventies, Texas Instruments was the first to put several transistors, resistors, and capacitors in a single chip of silicon. That was the birth of the Integrated Circuit (IC). Soon, it was the Large Scale Integrated Circuit (LSI) and VLSI in the late seventies that came into existence. The Apollo 11 mission did not use any Integrated Circuits. All its systems consisted of transistor circuits on printed circuit boards. The late seventies saw the introduction of the microprocessor with its assembly language and mnemonics. The big players were Intel and Motorola with their respective 8080 and 6800 processors. These technological advances in processor technology and in magnetic storage technology led to the more widespread use of mainframes. Then there were minicomputers and microcomputers. The microcomputer was later named Personal Computer (PC). The relational database (RDBMS) was developed for computers during this time and this led to real-time transaction processing for businesses, as against batch processing. Thus, a need for a central database in each enterprise was felt.

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In the nineties, Oracle and Sybase competed for the database market. Microsoft joined the fray in the 2000s. Towards the end of the Clinton presidency, the Y2K hype was unleashed. Several software companies including IBM made their money fixing the year 2000 bug primarily in COBOL and other legacy programs. The 2000s saw the rise of outsourcing of business processes, especially call centers and back-office work. A lot of outsourcing went to India, among other countries. Local Area Networks, especially from Novell Netware, became popular. The email and Internet (WAN) came into the picture. Until it was only LAN, data security was not a big issue, except for employee discipline. Though initially the Internet was used to share information similar to brochures to anyone who had access to the internet and to send instant emails, pretty soon it was making the reach of any computer application global. Anybody anywhere in the world could access a business application and perform transactions. The travel agencies didn’t need dedicated lines to various airline’s mainframes anymore. They could access the applications using the internet, which became a common/public data highway. The common communication medium also brought in problems such as hacking, viruses, and threat to data security.

data-management

The database, especially the RDBMS, put into effect the concept of having data only one place across the enterprise. The importance thus shifted from computing to data. Master and transaction data started to be collected in large volumes and business processes were streamlined. Governments and enterprises, big and small started taking advantage of the database concept, and applications were developed in thousands. Often the programmers specialized in particular languages doubled up as analysts and organized business processes and data. Mr. Hasso Platner (founder of SAP) was writing MRP and other applications for various organizations on IBM mainframes. He was modifying his base code for each enterprise. Soon he developed a configurable program that could be set up for different organizations and thus, the R/3 ERP system was born. Oracle saw the business opportunity and developed Oracle Applications, later christened E-Business Suite.

The big ERP systems were deployed on-premise in big servers for applications, middleware, and database. Business and technical experts were needed in all major companies. Large amounts of data continued to be collected. Salesforce (Oracle had a big stake in it) made the Software as a Service model (later called Cloud Applications) popular and acceptable in the market. CRM was not considered a Core Application and salespeople purchased enormous amounts of Salesforce subscriptions. Now that the market was primed for Cloud Applications, Oracle introduced Oracle Fusion Cloud Applications. Though it was highly successful, Oracle has extended full support for the E-Business Suite R12 version all the way till 2030.

With all the data that has accumulated since the nineties till date, there was a huge potential for monetizing data asset(s), by selling (using) it externally or internally. The industry name for such monetization is Digital Transformation. Let’s explore Digital Transformation in further sections.

DIGITAL TRANSFORMATION

Some of the questions that are commonly associated with Digital Transformation are:

  1. Is Digital Transformation for People or Applications?
  2. Is a more connected world needed for Digital Transformation?
  3. Where and how does data come into play?
  4. What are the basic building blocks for Digital Transformation?
  5. Is it better to upgrade to Modern Applications or to build new ones?

Connected People

Connected Applications

Connected Data

Collaboration

There is a joke making the rounds, which has a good amount of truth in it. Who brought about digital transformation in our Company? You think it is the CIO, CFO, CTO, or CEO. No, it is the Covid19 virus. Yes, the virus brought collaboration among people onto digital platforms such as chat, video chat, shared documents, and drives. Real organizational Digital Transformation demands more than “collaboration of employees”. It demands “Collaboration and Coexistence of all Business Applications”. It is a connected world after all. Portals-based software (State of the art collaboration between multiple companies, people, and application) are excellent for collaboration and eradication of duplicate efforts. A lot of the tedium can be shifted over to the computer. Nowadays portals need not be confined to suppliers, customers, employee HR, etc. Their power can be massively used to improve productivity by providing portals for each business role, one role at a time. These portals would feed the backbone of ERP systems.

Application to application communication (Master Data and/or Transactional Data) in real-time or batch is very much desirable in any enterprise which uses a multitude of best-of-breed applications.

Born Digital versus Transformed

Many businesses were born “Digital”. For example, Amazon, eBay, Netflix, Roku, Google, Salesforce, PayPal, Expedia, Priceline, etc. They do not have an extensive network of employees to provide products or services to their customers. Most of their products and services are delivered over the web and income is realized electronically in return. Amazon started as an online bookseller. But over the years they converted many of their software processes and internal infrastructure management into saleable products. Many other companies existed long before “Digital” was a popular word. For example, Kelloggs, IBM, GE, Comcast, at&t, John Deere, Fiat Chrysler, Shell, Marriott, and many others. At times, the nature of the products and services they sell, do not allow them to jump into Digital Transformation. They can sell a car online, provide a lot of digital services inside the car, etc., but they have to do a lot of collaborative people teamwork to produce. When they use robots to manufacture their cars, they are doing some Digital Transformation. When they bring all their (most of their) Supply Chain Streamlined into a computerized system, they are undertaking the Digital Transformation journey. Moving tasks from the mechanical and human realms to the computer and digital realms, always provided cost savings, efficiency, and more profits. Compare the cost of making a mechanical grandfather clock vs that of a digital clock. Compare the cost of entertaining a group of people in a theater versus throwing bits and bytes at them through the internet and charging them for a digital movie rented on a streaming site. Storing information (books etc.) is cheaper in electronic format than paper. Digital transformation is not always a formula for success. The customers have to be ready for it.

Data-Driven Enterprise

Digital Transformation is essential to participate and win in the Data-Driven Economy. It was also known some time back by the name “API-driven Economy”, aka “Connected and Communicating Applications”. Data-Driven and API Driven, point to several digital applications and web services complementing each other and contributing to the business objectives. That means they are connected together, they talk to each other, and thrive on inputs from other applications. The synergy generated by the careful orchestration of all Business Applications is very useful and powerful. For example GPS and maps-based computer outputs and displays. They’ve made life easy for the general public and improved productivity in the business world. Another example would be monitoring your fleet of trucks across the country or continent.

Somewhere here we have to realize that “Digital” is just a buzzword to make people buy your stock. It is really the applications that are the stars in this transformation. We have been “Digital” since the mainframes of the 1960s.

When we agree to transform our Applications to participate in the “Data-Driven Economy”, we have to make sure the organizational data is clean, that it has a sufficient record of the past within our organizations, and that we are willing to bring additional insights from vast amounts of sentiments expressed by consumers on social media.

Customer Experience

Operational Agility

Culture and Leadership

Workforce Enablement

Digital Technology Integration

Digital Transformation should lead to “Operational Excellence” of the Organization and also “Decision Making Excellence”. Do we go on a boating trip without checking the weather app for rain and wind? We don’t. Then why as a business leader, would I be satisfied with minimal data inputs and hunches and gut feelings to make decisions. I should demand high-quality reports and facts in various dimensions.

Know thy data. Big enterprises have evolved over many years and through many acquisitions and mergers. They end up with hundreds of operational and analytics systems and huge amounts of data spread across. It is good to catalog the data and find it when needed.

Know thy Customer better. By knowing all aspects and different facets of the customer from different angles, we will be able to hold a more meaningful conversation with them and generate sales.

Going Digital

Digital Transformation is not about seeking one great and mighty application or building one, but about having a Digital Platform, which is flexible and allows the reuse of the various components. Analogy: Buying a Mattel Toy Car vs buying a Lego set. With the Lego set, you can build a car, which tomorrow you can reassemble into a “crane” or “clock tower”. ChainSys Smart Data Platform is similar to the Lego set. The pieces connect well amongst themselves and APIs of external ERPs/Enterprise Applications.

Schneider Electric reduced from 150 ERP Systems to 12 ERP Systems.

What is good for one industry may not be good for another. For example, Sentiment Analysis by retail giants, fashion boutiques, and designers can fetch immense returns. A software products company may not be able to leverage it, just by the fact that their business is B2B and not B2C.

Infrastructure and Building Blocks

Some of the ingredients to achieve Digital Transformation have been around with us for a long time. Robust and secure networks on the infrastructure side. Protocols to exchange information via Web Services. It is time we cook up something tasty with the ingredients. Put them together to work for our organizations. Chain-Sys has wrapped 1000s of APIs into Web Services, covering Oracle Cloud Applications, Oracle EBS, SAP ECC, SAP S/4HANA, Salesforce, Microsoft Dynamics, and others. That means you as an IT person can accomplish a lot with drag and drop, no code low code programming.

Moving your hardware and networking infrastructure to the cloud does offer reduced TCO (Total Cost of Ownership) and better network security and frees up your mind to focus on core business issues and problems.

Applications

  • Typical Applications Scenario: Best-of-breed ERP systems on Premise, on Cloud, or Hybrid.
  • Top CRM and HRMS Applications on Cloud, Collaboration, and Productivity Applications from Microsoft, Google, etc.
  • In-house legacy systems, our website, social media and online commerce, portals provided by our suppliers and customers, internet sites, and apps we frequently refer to.

Apart from data integrations/data interfacing using templated Web Services from ChainSys, you can also orchestrate BPA (Business Process Automation) using Smart BOTS™. Hey if you find attacking through the backend difficult, attack through the front end. The same idea is used in automated testing.

Digital Transformation

The challenge is how to make these applications interact, with meaningful human oversight or governance.

Digital Transformation

One of the ways to quickly harvest the benefits of Digital Transformation, without much sweat and money, is to upgrade to the new offerings of Applications Vendors, preferably on the cloud. ChainSys has the tools to migrate your setups and data to the Cloud Applications. At the same time be aware that Customizations are going to be difficult and expensive in the Cloud environment. (You can check with ChainSys for some tricks to get Customizations to work for you fast and simply.

Digital Transformation is more than implementing or upgrading to a single great Enterprise Software. You need a flexible and easy-to-use Digital Platform, which can make your dream projects come to fruition fast and economically. Such a platform would allow you to use various low code no code features and reuse the components you build (think Lego building blocks). For example, the data quality you build for MDM should be available come time for data migration.

We just cannot get up and say one day, “Let’s buy all the Digital Transformation Software and Tools from Microsoft or Oracle for that matter.” We will end up just as a test and bug reporting center for those companies. We have got to be selective and integrate them well with the existing infrastructure, technology, and people.

As a software provider, ChainSys offers the following: Ready glue adapters between most enterprise applications, batched or real-time data exchange, governance, data quality Improvement, quality data to data lakes, AI/ML algorithms to predict business outcomes, planning engines, ability to visualize data in pleasing charts and diagrams, BOTs to do regression testing, etc. We provide the test recordings for the popular ERPs and BOTs to do Business Process Automation, involving multiple Applications. Basically, get/read data from one application, and load it into another application without human intervention, maybe with some amount of supervision.

Some companies are “Born Digital” and others achieve “Digital Transformation”. ChainSys’ objective is to bring “Digital Transformation” in a very flexible, effective, and cost-conscious manner to all the hard-working and excellent companies of the world.

Happy transformations to you all.

The FUTURE:

I’d like you in the audience to predict good stuff for humanity and planet earth in the near future and send them to prediction@chainsys.com. I will select the top three predictions, share them with all and also offer winning prizes.

Here is my prediction: As the pendulum has swung to the extreme end of globalization, data and applications shall drive the world back towards a more sensible, sensitive to the local environment, and personalized experience. The supply chain will adjust accordingly. Customer Experience (CX) will reign supreme. Technologists and programmers will chase away tedium in the workplace and make it more fun using collaborative portal-based applications.

AUTHOR BIOGRAPHY:

Ganesan is a postgraduate from the Electrical Engineering department of IIT Chennai with his bachelor’s from the Electronics and Communications Department of AC College of Engineering and Technology. He has extensive experience in VLSI design, Software Systems Design, and Development, Project Management, Marketing, and Business Administration, and Contracts. Currently is the Executive Director of Chain-Sys Corporation, USA. Before his current role with Chain-Sys, he was associated with Unisys Corporation, DSRC, Tata Unisys, and Bharat Electronics. Presented a paper on “Test Patterns” at the First International Conference on VLSI Design held in IIT Chennai (1984). He has traveled widely in the USA, India, Canada, Europe, Singapore, and the Middle East. His other interests are Story Telling, Creative Design, Painting, Swimming, Hiking, Canoeing, and Kayaking. He speaks Tamil, English, Hindi, and a little Telugu and Kannada.

Data Estate: Building Sophisticated Data Infrastructure Through Modern Technologies

Data Estate: Building Sophisticated Data Infrastructure Through Modern Technologies

Data, pandits say, is now the most valuable resource on the planet. Even more than oil. With the world advancing daily and adopting newer digital technologies with every passing minute, it hardly seems like an exaggeration. Today, data is the most valuable currency a company possesses.

Today’s organizations deal with voluminous data on a daily basis. Whether it’s data generation, storage, or harnessing data to make business decisions, data plays an integral role in multiple operations that are vital to the functioning of these companies. Different teams have access to, handle, and analyze this data on an ongoing basis, making data management a cumbersome task.

What is Data Estate?

Companies are drowning in data coming from heterogeneous sources and struggling to find a solution to structure it all. That’s why a term called Data Estate is trending in the industry nowadays.

Data Estate is nothing but all the data, from all the sources, that a company owns and is usually what needs modernization. It’s simply to build a data infrastructure, through modern technologies to ingest and store all your data with governance and quality protocols in place. This can be developed on-premise or cloud. You can even get the best of both worlds by using a Hybrid approach depending on the business requirements. This helps make organizations proficient in storing, managing, and leveraging your data for analytics including business applications, IoT, departmental data, and more!

How to Get Started:

Before you begin towards building a successful data estate, there are a few key questions that should be answered which will guide you in your journey of implementing an infrastructure.

  1. What is your end goal that you want to achieve?
  2. What are your short term and long term business requirements?
  3. What are your current Business processes and how do they integrate with your current data processes?
  4. Who will have access to the data and which data will these persons have access to?
  5. Would you like to go On-Prem, Cloud or Hybrid? Why?
  6. What will your construction Partners look like?
  7. It is a complex and challenging project. Make a roadmap with smaller tasks to achieve the end goal.

Seems overwhelming? But it’s really not. ChainSys will be able to help you answer all these questions and build the entire infrastructure for you including automation. Our end-to-end data management platform ensures data access in a clean and secure manner while standardizing all data to minimize the risk of data loss, duplication, etc. This ultimately translates into real-time results which harness the true value of your data while saving you money, time, and resources.

How ChainSys Can Help:

With over two decades in the industry, we have helped a plethora of clients from various industries including, energy, aviation, finance, technology, manufacturing, and more! Our data solutions are 360-degree right from the beginning, all the way to the end, some of which include:

  • Migration & Integration
  • Quality, Cleansing & Governance
  • Active Metadata Management
  • Enterprise Data Management
  • Analytics and much more

At ChainSys, our approach is customer-centric. We begin by understanding all the above requirements and build a customized team of experts that are experienced with your industry and uniquely suited to tackle your specific challenges. This team will then dive deep into your business and data processes, and your legacy systems and do all the necessary homework. Following which, we will not only draw out solutions specially designed for you but also build a roadmap to help us achieve your goals together, in a timely fashion. 

If you’re looking to level-up your data management game, get in touch!

To view all the Solutions we offer, click here.

Smart-data-platform

3 Ways to Continually Improve Your Data Governance

3 Ways to Continually Improve Your Data Governance

A wise man once said, “with great power, there must also come great responsibility.” While he wasn’t talking about data, he might as well have been! A company’s data is like its lifeblood—you must keep it safe, secure, and well-regulated if the company wants to thrive. Data governance is the responsible path toward data security. Read on to learn three ways to continually improve your data governance.

Defining Data Governance

Data governance is a process, but not one that you undertake just once. Data governance must constantly be in place in your company to ensure data protection. At its simplest, data governance defines the regulations you enact to monitor and control data within your organization.

Establish Your Framework

The first step to improving data governance is to set up your framework—how do you want your data governance to work? It should be simple and easy to see who has access to what data. Also, develop guidelines to show how to measure successful data governance for your company.

Set Up Controls

Once you figure out the big picture, it’s time to dive into the specifics. Data controls are the rules and regulations that dictate which employees have access to certain information. Whenever any employee accesses a piece of data, that moment gets cataloged and recorded for future reference.

Monitor Your Data

That’s where data monitoring comes in. Data pipeline tools help companies keep track of data use in case anyone does something with your data that they aren’t supposed to. When you realize there’s been a breach, it’s simple to work back to find out who accessed that information so you can handle the situation appropriately.

Now that you know these three ways to continually improve your data governance, don’t hesitate to take your business to the next level. Whether you improve data governance and security or data integration and analytics, each change makes you more competitive and viable in an ever-changing business environment.

3 Signs Your Company Needs a Data Analytics Solution

3 Signs Your Company Needs a Data Analytics Solution

Data is a simple fact of running a business in this day and age. However, you must understand that data is more than facts. What good are a million customer insights if you can’t convert numbers into actionable information? We have the answers! Read on to discover three signs your company needs a data analytics solution.

Tons of Meaningless Data

A dataset consisting of tens of thousands of data points is potentially very useful—and potentially very useless. A pile of numbers is meaningless unless you have a way to parse the data and glean helpful insights into your customers’ habits.

If you have more numbers than you know what to do with, data analytics can help guide you to understanding. This is the single largest benefit to data analytics—it’s a way to make sense of the proofs and figures arranged in columns before you.

Reports Come Too Slowly

Many businesses rely on up-to-the-minute information to stay agile in a competitive space. When you learn about a new trend that could benefit your company, it makes sense to pounce right away so you can capitalize.

Unfortunately, when that information arrives on your desk too late, there’s not much you can do. Data analytics ensures you have the reports you need when you need them.

Old Spreadsheets Aren’t Cutting It

When you scour through the old spreadsheets in your data catalog, you may find them sufficient. However, where Excel once served you well (when you had far fewer fields to fill), you now find that populating an Excel spreadsheet is time-consuming and far from flawless. Data analytics provides an alternative without the possibility of an invalidated report due to a single incorrect formula.

Now that you know these three signs your company needs a data analytics solution, come up with your own solution and start benefitting from new insights. It won’t take long to see results, so set the wheels in motion and enjoy the results!

The 7 Best Data Management Tools for Your Business

The 7 Best Data Management Tools for Your Business

Just as every woodworker would tell you certain tools make all the difference, so too would data analysts. Picking the right tool for the job ensures you get the answers you’re looking for in a timely manner. At this point, however, you may not even know which questions you should be asking. Read on to learn what data management tools are, as well as the seven best data management tools for your business.

Data Management Tools Explained

Before we talk about the best of the best, it’s important to have a solid understanding of what data management tools can do for you. “Data management tool” is a broad term that can relate to anything in the data space, from storage to synchronization to migration to analysis. In short, data management tools make working with data easier.

Key Features

What does a good data management tool look like? First, it should be secure. You’re handling sensitive data, whether it’s internal information or data you’ve gathered from your clients. Either way, you don’t want it escaping your bubble. The data management tools you choose to work with must provide rigorous levels of security and privacy.

In addition, a data management tool should afford you some flexibility. You may be a small-to-medium-scale business right now, but what about five years from now? If you eventually end up with an additional 100 terabytes of data, you want to pick tools that can handle those larger numbers.

You should also seek out tools that facilitate global work. You never know when employees will travel for a conference and need to log in for data analytics. Your tools must allow access to your information no matter where you are.

Finally, data management tools should be intuitive. Any great tool will require a little time to learn, but it shouldn’t take your employees away from their regular jobs for weeks at a time to understand a new system—the best tools make learning a simple and efficient process.

Different Types of Data Management Tools

There are four main categories of data management tools. These categories include master data management tools, cloud data management tools, data visualization and analytics tools, and ETL and data integration tools.

Master data management is all about visualizing sets of information across your whole organization, while cloud data management is built to help you manage your data in the cloud and connect to various data sources.

Meanwhile, data visualization and analytics tools help you explore large sets of data and gain insights that can guide you to success. Finally, ETL and data integration tools are made to load data to a certain database and aid in the transformation of data.

Consider the following data management tools:

1. dataZap

dataZap can help you with everything from data migration to reconciliation. Data transformation is a touchy process that requires a sure hand. dataZap guides you through the migration process like no other tool and allows you to stay nimble with your data.

Since it’s a single platform that handles all your reconciliation tools, integrations, data ingestions, setup migrations, and archival, your team doesn’t have to spend hours learning different programs. On top of that, dataZap comes with a data quality engine so you don’t need to worry about working with clean and correct data—you’ll already have it.

2. dataZen

dataZen is an all-in-one data management solution that covers master data management, quality, and governance. When you want to ensure you’re working with high-quality data, dataZen can put your mind at ease. With agility and customizability, you can rest assured knowing you have the governance and business discipline you need.

All of this comes with little to no programming needed—the tools at your disposal should work for you, not fight you at every step. If you want to de-duplicate and consolidate data, choose dataZen for the most easygoing experience.

3. dataZense

Data analytics has never been simpler than with dataZense. It’s no secret that the toughest part of the job is interpreting and analyzing data, so dataZense gives you insights through easy-to-use—and understand—dashboards. There are over 3,000 prebuilt dashboards that will help you analyze and visualize your data, along with metadata crawlers that work using machine learning.

4. Tableau

With fast and easy connections to multiple data sources, Tableau makes a good case for being your go-to choice for visualization. Tableau boasts quick access to visualizations for your teams and clients, as well as a dashboard that’s intuitive to set up. As if that wasn’t enough, Tableau also makes it easy to explore your data and find what you need in a timely manner.

5. HubSpot

HubSpot’s main focus is on inbound marketing, but it has several features that extend beyond this goal. With analytics and reporting, it’s smooth sailing whenever you want to organize or visualize your marketing data.

6. Oracle Data Management Suite

Thanks to its years of impeccable service, Oracle is one of those data management companies everyone knows about. When you need a powerful platform that brings governance, visibility, and standardization under one roof, Oracle data management is the way to go.

Duplicate data isn’t a factor you need to worry about when you use Oracle data management, so consolidate your data and bring everything into one comprehensive place.

7. Microsoft Data Management Suite

Microsoft is a big name in big data, and for good reason. The Microsoft data management suite is a comprehensive set of tools that can drastically increase efficiency while reducing costs. With seamless data integration and migration, synchronizing your apps with other systems is a quick and easy matter.

When you’re looking to migrate data from legacy applications or manage data across domains, Microsoft data management makes everything simple and streamlined. Risks are always a factor when migrating, but Microsoft’s tools reduce risks and save over 40 percent of the time spent on a normal migration.

Now that you know the seven best data management tools for your business, choose the ones that work best for you. This simple change to your workflow can make a world of difference for your company.

The 5 Key Types of Data Analytics Every Business Should Know

The 5 Key Types of Data Analytics Every Business Should Know

In this brave new world of big data, it can feel like a challenge to stay on top of all the insights you’re told you need. We’re here to make things a little simpler by breaking down different types of data analytics that your teams will come across. Read on to learn the five key types of data analytics every business should know.

Descriptive

The simplest way to think of descriptive analytics is as statistics. Before you can start deciphering the meaning of your data, you need to collect information. The information you collect will differ based on your goals. For example, if you want to know about sales trends, you might collect sales information from a particular item to learn that it sells far better in summer than in winter.

Diagnostic

Diagnostic analytics is all about taking a closer look at your data to find the root causes of things. With diagnostic analytics, you observe data points one by one until you can determine whether that individual piece of data has the effect you’re searching for.

Predictive

Predictive analytics is self-explanatory—you’re looking for a forecast of things to come. Based on information from the past, what trends will be hot in the coming season or year? Predictive analytics alone can be powerful, but you’ll see the best results when you couple it with prescriptive analytics.

Prescriptive

Prescriptive analytics takes predictions and tests out all the options before suggesting the best course of action. Salesforce data management and prescriptive analytics can help you avoid pitfalls while staying on the optimal route to your final destination.

Augmented

Augmented analytics takes the best aspects of artificial intelligence and machine learning to provide automated data preparation as well as insight discovery. It takes predictive and prescriptive analytics, combines them, and gives you your answers in the snap of your fingers.

Now that you understand these five key types of data analytics every business should know, we hope you have the tools you need to take your business to the next level.