Chain-Sys: What Is a Software Product Company?

Chain-Sys: What Is a Software Product Company?

Computers have been around for a long time, but the general public still doesn’t fully understand how they work. Because of that confusion, there are often many questions surrounding companies that offer computer products and services, especially when those products and services are amorphous. Read on to learn what a software product company like Chain-Sys is.

Software vs. Hardware

The first (and most important) thing to understand is the difference between software and hardware. It’s in this distinction that a lot of confusion around the nature of software comes from. You may think about hardware stores when you hear this term—in that case, hardware refers to things like nails, screws, and cabinet fixtures.

In this case, however, hardware refers to all the physical components required for a computer. This includes the RAM sticks, CPU, video card, hard drive, and more. On the other hand, software is the programs and processes that tell those pieces of hardware what to do. Your web browser, for instance, is a piece of software on your device.

Software: Product or Service?

With that said, many people aren’t sure whether companies that offer software provide a product or a service. That discrepancy is understandable, as products are traditionally known to be physical goods. We believe the best way to look at it is that software companies provide products and services. The design and upkeep of software is the service, while the software itself is a product (albeit a digital one).

Software Company Services

In addition to developing and maintaining software, many software product companies also offer IT services so that you can ask questions about the software. Some software companies may also provide services or products to assist with cloud data management or other processes—it depends on the company.

Now that you know what a software product company like Chain-Sys is, we hope you better understand what we do. If you have any remaining questions, we’re always happy to talk. Contact our team today!

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.

data-management

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.

What You Need to Know About Intelligent Data Migration

What You Need to Know About Intelligent Data Migration

Data migration is one of those things—you know that it has to happen, but you never want to do it. Migrations offer up a whole host of problems, from potential downtime to the struggle of learning a new system. As with many headaches in the business world, someone thought to themselves, “There has to be a better way!”

That person created intelligent data migration, and you’re probably going to be very happy they did. Let’s take a look at the old way of doing things and the reasons this new version is far better. Here’s what you need to know about intelligent data migration.

Lifting and Shifting

Data migration hasn’t always had the opportunity to be “intelligent.” The data migration you’re used to probably makes use of the “lift and shift” method, which is exactly what it sounds like. Essentially, your teams take data from one system, pick it up, and move it to the new system. This is obviously a bit reductive, but it provides you with a good idea of what goes on during the migration process.

To dive a little deeper, your teams have pored over your data to find the fat that you can trim and sought to improve organization during the migration. However, there’s only so much that humans can do when it comes to categorizing and sorting petabytes of data.

Big Bang vs. Trickle Migration

We can further break down the “lift and shift” method into two main types of data migration: big bang and trickle migration. With big bang migration, the entire process happens in one go—you push a button, and migration begins. That migration doesn’t stop until you port over all your data to the new system.

The main drawback of big bang migration is that it renders your systems useless during the process—all your computing power goes into migration. Unless you can afford days or weeks of downtime, it’s usually better to choose trickle migration.

Trickle migration, on the other hand, has a “slow and steady wins the race” mentality. Your teams will come up with the optimal order to transfer data to your new system, moving forward one piece at a time. With trickle migration, you don’t need to worry about downtime—your old system is still functional during the shift.

While both of these types of migration do the job, they require a lot of brainpower from your team and are likely to leave you with problems in your new system. This isn’t the fault of your team, but it’s because different systems work differently. You never know when a particular piece of data won’t sit right in a new system.

Fortunately, these problems are annoying but correctable. And, even more fortunately, intelligent data migration can help you avoid these problems altogether.

What Is Intelligent Data Migration?

As with many other “smart” processes, the difference between regular and intelligent data migration is the addition of artificial intelligence. AI will discover your data—it will look for all the different types of data at use in your business.

Artificial intelligence then classifies that information based on how critical it is. The determining factors include both the importance to your company and its importance in the data privacy regulations hierarchy.

The main idea of intelligent data migration is to use insights generated at fast speeds to sort and organize data efficiently. Once the artificial intelligence has delineated where the data is going, you can perform a low-risk migration at the optimal speed.

How Does That Work?

It’s all well and good to say, “Artificial intelligence makes magic happen,” but what is really going on behind the scenes? In a way, the actual action that your AI is taking isn’t any different from what a human would do to organize data. However, AI can do it much faster.

Essentially, the AI looks over metadata to find out what type of document it’s dealing with, how old the document is, the file size, and who has access to the document. Using all this metadata, the AI can determine where the data needs to go—either ported into your new system or left behind if the information is unnecessary.

Why You Should Use Intelligent Data Migration

You know that AI can help you speed through data migration, but why else should you use intelligent migration? We’ve made a list below of all the benefits of incorporating this technology into your strategy.

Reduced Storage Costs

One of the biggest benefits of using these data migration tools is that you can save money on data storage. Intelligent migration will find a ton of redundant information that you can leave behind—and with each piece you leave, you free up more storage space. By the time the AI looks over everything, you may need a lot less storage space than you assumed.

There’s no need to pay a premium to keep obsolete information on hand. Open up your IT budget and breathe easier when you see the bottom line.

Reduced Migration Costs

Data migration isn’t cheap, but when you migrate fewer documents, the process takes less time. In addition to saving storage money, you’ll also be able to make use of your new system sooner. On top of that, many AIs require training before you can use them. Luckily, intelligent migration AI comes pre-trained, so you don’t need to spend any time doing that.

Better Productivity

Once everything is in the new system, your employees will be able to work more efficiently with less unnecessary information clogging up the system. It’s far more difficult to get things done when you need to sift through old files before you can begin.

Compliance

Proper data storage isn’t only a matter of convenience—there are also state and federal mandates that require you to classify data in a certain way. To avoid fines that come about through human error, let artificial intelligence do all the work!

Now that you understand what you need to know about intelligent data migration, decide whether it’s a tactic you want to employ at your facility the next time a migration needs to happen. There’s nothing better than efficiency, so talk with your teams and figure out if intelligent data migration is right for you.

What Is Data Integration and How Does It Work?

What Is Data Integration and How Does It Work?

The free exchange and use of information is an essential element of any business that requires data in order to succeed. Unfortunately, many businesses of all shapes and sizes have problems because they use different systems to carry out various processes.

This strategy in itself makes sense—you want to use the best applications for finances, platform building, and sales—but these programs don’t always come in the same box. However, when you pick and choose applications across a business, it creates roadblocks between departments and works against the easy flow of data. Read on to learn about data integration and how it works to solve this problem.

Defining Data Integration

Data integration is the process of taking data from several sources and combining everything into unified datasets. This has uses both operationally and analytically, and it is often the first step in a wider data management process.

The objective of integration is to create a set of consistent data that is usable for everyone within your organization. Once all the data is integrated, you can use it to gain valuable insights and solve problems in new ways.

There is no limitation to which industries can use and benefit from data integration—if your company has data from disparate sources, integration can help you consolidate and solidify your strategy. When you have information from more sources, the value of your data skyrockets.

How Does Data Integration Work?

With data integration, there are data sources and target systems. Integration pulls data from various sources and routes them into your target system, performing simple cleaning processes along the way to prevent duplicate or redundant data.

For this reason, data integration is typically more complicated than simply directing numbers on a spreadsheet to move to a larger spreadsheet. A program must scour the different datasets to ensure you aren’t left with unnecessary numbers or inaccuracies. Who would have thought combining datasets could be so tricky?

Data Integration Solutions

Now that you have an understanding of data integration, let’s take a deeper look at the problems integration works to solve.

Big Data

Big data is just what it sounds like—vast quantities of data. You’ll hear a lot of people say, “the more data, the better.” While that sentiment certainly has a basis in truth—if you have more data about your customers, you can better tailor your products and advertisements to them—it comes with its share of conundrums.

The two issues that surface regarding big data are data volume and data variety. A robust system takes care of the volume problem, but variety is still an issue—enter integration. Integration helps you organize and decipher vast quantities of data, no matter where that data comes from.

Semantics

A massive issue with rudimentary integration, or integration without cleaning and consolidation, is that you’re likely to be left with duplicate data. Even simple integration systems can catch exact duplicates, but what happens when different pieces of data describe the same thing in different ways?

For instance, perhaps one source comes from overseas, where dates are listed as DD/MM/YYYY; meanwhile, another source comes from the United States, where dates are written as MM/DD/YYYY. You don’t need both variations, so integration steps in to clean things up. This process makes it easier to recognize patterns and avoid being bogged down with unnecessary information.

Data Silos

Data silos are a thing of the past that we’re still tripping over today. As with many other data solutions, you can see the good intentions of data silos while also feeling frustrated by the problems they cause. Essentially, data silos refer to data sources stored in specific locations.

Say a company has a Toledo branch and a Tucson branch. Each location has its own unique set of data that the larger company doesn’t have easy access to since the data is stored on-site. Integration—especially cloud data integration—can take those unique data sources and bring them together in a way that your entire business can take advantage of.

Accessibility

Why create more work than you need to? The best strategy is to make something once, then deliver it to as many people as need it. One central data source eliminates redundancy and reduces confusion while also promoting collaboration.

Benefits of Data Integration

Now that you’ve seen all the problems data integration solves, let’s consider some of the integration’s benefits.

Reduced Costs

The more processes you can automate, the less your employees will need to work to complete tasks. This opens up their schedules to complete new and different tasks, which means you can continue to pay your employees the same amount while receiving more deliverables. The longer the human workflow, the more you end up paying.

Efficiency

Building on the previous point, this boosts the efficiency and productivity of employees companywide. Where some workers could only focus on basic, busy work tasks, they now can dive into more complex work. This gets your customers the results they’re looking for while providing more fulfilling work for your employees.

Better Data Quality

When you work with clean data, overall quality increases. It’s easy to struggle through data with redundancies and inaccuracies without really realizing what’s happening until a computer signals otherwise. All incoming information is validated with integration, which means you don’t need to worry about creating unnecessary roadblocks for your employees.

Upgraded Decision Making

No good decisions are made based on a lack of information. With integration, you and your employees will have everything needed to make educated choices for your business. This is why integration coupled with data analytics is the ideal team—the easier your data is to parse, the better your insights will be.

Enhanced Customer Experience

No one wants to wait around to get answers. With integrated data, your customers will receive the information they’re looking for without spending a lot of time doing it.

Now that you know what data integration is and how it works, consider adding data integration to your information pipeline. Whether you have multiple data management systems or a single coherent system, the integration will bring all your information together for a prime view of all the information at work in your business.

How To Plan a Migration From a Non-SAP to an SAP Database

How To Plan a Migration From a Non-SAP to an SAP Database

Migrating data is a complicated process and one you must get right to set your business up for success. Unfortunately, it’s easy to create problems in a data migration without proper time and effort. With that in mind, consider how to plan a migration from a non-SAP to an SAP database, as well as why you may want to make the switch.

Why Move to an SAP Database?

SAP HANA is a database, but it’s often referred to as a “data platform.” This is because SAP HANA offers much more than a traditional database, which is also why it has become so ubiquitous in the data world. If you’re looking for an application that can perform intense processes without causing a strain on your hardware, HANA is the answer.

A major difference between SAP HANA and other data platforms is that HANA uses in-memory processing. Instead of relying on your computer’s hardware—and the application you’re using—to perform processing and calculations, analysis happens in the database itself. Aside from that reason, why should you move to SAP HANA?

Big Data

Big Data is a trend that’s here to stay. Big Data has more of everything in both quantity and quality. The clearest way Big Data differentiates itself from data is in the three Vs—Big Data has greater variety with larger volume and higher velocity. Unlike other databases, SAP HANA can handle those three Vs with ease.
As the data landscape changes, companies that refuse to evolve with the times may find it difficult to keep up with others. Therefore, jumping into Big Data headfirst could be precisely what your business needs to stand out.

Scalability

“Scalability” is one of those business-jargon terms that’s actually more important than you might realize. Stasis is the death of a business, so solutions that provide room for exponential upward growth are invaluable. SAP HANA is easily scalable as your company grows and changes, meaning you can always be improving. Additionally, this is something that, like it or not, your shareholders need to see.

Currently, you can grow your SAP Business Warehouse up to a whopping 168TB of RAM. You read that right—not storage, RAM. To put it in perspective, that’s over 800 times the processing power of the average NASA computer. Suffice it to say, SAP HANA is more than capable of growing with you!

Real-Time Analytics

Not every business needs access to real-time analysis, but if yours does, you really need real-time analysis. SAP HANA is capable of providing up-to-the-minute information using its in-memory processes. Even basic HANA setups can process information several times faster than non-SAP databases. Simple reports will be done in the blink of an eye, while more comprehensive analyses will take perhaps two blinks.

Mobility

SAP HANA is nothing if not flexible and versatile. Your team will not need to leave their trusted interfaces behind when you switch to HANA, as one of the best things it boasts is compatibility. You can also easily apply HANA’s analysis to your old models. You may discover that old assumptions were off, meaning you can pivot your direction in response.

The Main Migration Types

The two main types of data migration are big bang and trickle migration, and each comes with its own benefits and drawbacks. Big bang migration is just like it sounds—you can perform your whole migration in one fell swoop. This usually leads to an operational system much more quickly, but with the potential for many more snags and issues along the way.

Trickle migration requires more thought at the onset as you must develop a plan to migrate parts of your system one at a time. This slow-and-steady plan allows you to keep both systems operational as data is transferred and helps avoid troubles and conundrums in implementation.

Trickle migration is generally recommended, but some companies want migration over and done with and use the big bang. Impatience is rarely a boon, so be wary of quick fixes.

Design a Migration Plan

The first step in any migration plan is to determine your migration goals. What technical specifications are you hoping to reach, and what do you want your new system to do that your old one could not? Once you’ve answered those questions, it becomes simple to begin the design process.

This initial stage is crucial for your final result, so ensure your team works diligently and documents everything to mitigate risks and reduce oversights. Even the smallest hiccup at this stage, if left unresolved, can create a massive headache later on.

Build Your Foundation

The second phase is all about executing the plan made in the previous step. While your team isn’t beginning the migration process yet, this is the time to lay the groundwork for migration day—or, more accurately, migration weeks. Your team should prepare equipment for the transition, set up security for data protection, and build whatever is needed to traverse your new space.

Begin Migration

Once the foundation is built, you can commence migration and begin to see the fruits of your labor. Traditionally, this will begin with all your non-critical systems. If you have systems in a sandbox stage, these should be transitioned first. Again, your migration plan is dictated by the plan you made in the first stage.

Even though it may be tempting to switch things around now that you’ve reached the end, do your best to stick to the plan you made. After all, you made it that way for a reason. Maintain a runbook during the transition so you can apply what you learn from each tier of migration to the next.

Optimize Your System

Finally, once the system is up and running and SAP migration is complete, you should prepare a team to optimize the system. Just because all your data is in a new place doesn’t mean your work is done. There are plenty of things left to do in order to create the most efficient system possible. Also, this stage won’t be super speedy, as it will likely take place over the first few months of operation.

Now that you know how to plan a migration from a non-SAP to an SAP database, set your business up to handle bigger data and improve your company’s scalability exponentially.

3 Oracle Data Migration Mistakes To Avoid

3 Oracle Data Migration Mistakes To Avoid

Data migration is a necessary part of any company’s life if they want to grow their business. We often see businesses realizing that their current database doesn’t have the structure and technology to support them for years in the future, so they switch to a new system like Oracle. Read on to learn three Oracle data migration mistakes to avoid.

Underestimating the Necessary Changes

A new database means a lot of changes. Even though there are some similarities across databases, you can’t avoid organizational and procedural differences. To hit the ground running with your new database, you must devote time to understanding how things will be different. Before beginning the migration, you should consider how management processes and support documentation need to change.

Not Enough Training

Just as you must consider changes from a managerial standpoint, you must also consider the differences for your employees. It will take time for your administrators to learn the ins and outs of the new platform, and they’ll need your support. Allow plenty of time for your administrators to become comfortable with new tools, commands, and utilities.

Choosing Big Bang Over Trickle Migration

The two main types of data migration are big bang and trickle migration. While each has some benefits, big bang migration has significant downsides. Essentially, big bang migration involves getting your whole migration process out of the way in one fell swoop. That may sound ideal, but it can leave you with a lot of problems with your data.

Trickle migration allows you to take things slowly, moving one system at a time. With this method, your Oracle data migration proceeds so that you’re able to correct any issues between batches of migration, leading to a more successful overall transition.

Now that you know these three Oracle data migration mistakes to avoid, set yourself up for a successful migration and enjoy your new database!