Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.

Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.

So what kind of information are businesses storing for marketing purposes? Some examples include:

- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.

Learning SQL development can seem like an overwhelming task at first. However, mastering just a few key points will help ease your way through 80 percent of the day-to-day challenges when writing stored procedures and solving common problems. Here are three important SQL development factors to keep in mind:

Outer Joins
One of the most crucial things to understand in SQL server are joins. Joins are a way to retrieve data from two or more tables based on logical relationships between them. Joins dictate how Microsoft SQL Server ought to use data from one table to select the rows in another table.

In my experience inner joins are intuitive while outer joins can present additional hours of grief by overlooking associations in the other table(s). The outer join is the key to answering questions about what the database does not have. For example, if you need to make a query to display all the students who are without report-cards, you’ll need a left join to get all students coupled with a “where clause” to return the ones who have nulls for their report card table columns in the results.

Many talented Java script programmers have muddled through the SQL Server by deficient coding around the inner join. As a result, their queries can take five hours to run, whereas, properly written left joins, can take only two seconds to run.

Aggregation
Grouping results comes up in SQL a lot more than you might think. Knowing how to write a query when answering questions such as, “What’s the average grade for each teacher’s student list?” is invaluable. This kind of question cannot be answered with a single table or solely by joins.  You’ll often find you need to use joins in conjunction with group by statements. Always write the raw query first and then look at the results. Next, you have to figure out the best way to group them, rewrite your select clause and add a group by clause in the end.

Digging Through Data
I find this is the most lacking skill in many programmers. In fact, many otherwise-talented programmers holding Master’s Degrees fail to get jobs because they couldn’t analyze rows of data objectively during interviews. It’s just something that’s not taught but is crucial to get under you belt. Why? Eventually, some query is not going to perform as you may expect. And, the only way to find discrepancies is to look at rows of data, identify what join isn’t finding a match or where bad data is throwing things into chaos. Get familiar with how joins actually work, even if you have to manually walk through the logic of a large stored procedure’s tree of joins. It’s boring and time-consuming but absolutely necessary.

Take the time to master the core skills that will make you a successful SQL Programmer and avoid queries that run for five hours!

When making a strategic cloud decision, organizations can follow either one of two ideologies: open or closed.

In the past, major software technologies have been widely accepted because an emerging market leader simplified the initial adoption.  After a technology comes of age, the industry spawns open alternatives that provide choice and flexibility, and the result is an open alternative that quickly gains traction and most often outstrips the capabilities of its proprietary predecessor.

After an organization invests significantly in a technology, the complexity and effort required steering a given workload onto a new system or platform is, in most cases, significant. Switching outlays, shifting to updated or new software/hardware platforms, and the accompanying risks may lead to the ubiquitousness of large, monolithic and complex ERP systems – reason not being that they offer the best value for an organization, but rather because shifting to anything else is simply – unthinkable.

There’s no denying that these are critical considerations today since a substantial number of organizations are making their first jump into the cloud and making preparations for the upsetting shift in how IT is delivered to both internal and external clientele. Early adopters are aware of the fact that the innovation brought about by open technologies can bring dramatic change, and hence are realizing how crucial it is to be able to chart their own destiny.

Over time, companies are migrating from COBOL to the latest standard of C# solutions due to reasons such as cumbersome deployment processes, scarcity of trained developers, platform dependencies, increasing maintenance fees. Whether a company wants to migrate to reporting applications, operational infrastructure, or management support systems, shifting from COBOL to C# solutions can be time-consuming and highly risky, expensive, and complicated. However, the following four techniques can help companies reduce the complexity and risk around their modernization efforts. 

All COBOL to C# Solutions are Equal 

It can be daunting for a company to sift through a set of sophisticated services and tools on the market to boost their modernization efforts. Manual modernization solutions often turn into an endless nightmare while the automated ones are saturated with solutions that generate codes that are impossible to maintain and extend once the migration is over. However, your IT department can still work with tools and services and create code that is easier to manage if it wants to capitalize on technologies such as DevOps. 

Narrow the Focus 

Most legacy systems are incompatible with newer systems. For years now, companies have passed legacy systems to one another without considering functional relationships and proper documentation features. However, a detailed analysis of databases and legacy systems can be useful in decision-making and risk mitigation in any modernization effort. It is fairly common for companies to uncover a lot of unused and dead code when they analyze their legacy inventory carefully. Those discoveries, however can help reduce the cost involved in project implementation and the scope of COBOL to C# modernization. Research has revealed that legacy inventory analysis can result in a 40% reduction of modernization risk. Besides making the modernization effort less complex, trimming unused and dead codes and cost reduction, companies can gain a lot more from analyzing these systems. 

Understand Thyself 

For most companies, the legacy system entails an entanglement of intertwined code developed by former employees who long ago left the organization. The developers could apply any standards and left behind little documentation, and this made it extremely risky for a company to migrate from a COBOL to C# solution. In 2013, CIOs teamed up with other IT stakeholders in the insurance industry in the U.S to conduct a study that found that only 18% of COBOL to C# modernization projects complete within the scheduled period. Further research revealed that poor legacy application understanding was the primary reason projects could not end as expected. 

Furthermore, using the accuracy of the legacy system for planning and poor understanding of the breadth of the influence of the company rules and policies within the legacy system are some of the risks associated with migrating from COBOL to C# solutions. The way an organization understands the source environment could also impact the ability to plan and implement a modernization project successfully. However, accurate, in-depth knowledge about the source environment can help reduce the chances of cost overrun since workers understand the internal operations in the migration project. That way, companies can understand how time and scope impact the efforts required to implement a plan successfully. 

Use of Sequential Files 

Companies often use sequential files as an intermediary when migrating from COBOL to C# solution to save data. Alternatively, sequential files can be used for report generation or communication with other programs. However, software mining doesn’t migrate these files to SQL tables; instead, it maintains them on file systems. Companies can use data generated on the COBOL system to continue to communicate with the rest of the system at no risk. Sequential files also facilitate a secure migration path to advanced standards such as MS Excel. 

Modern systems offer companies a range of portfolio analysis that allows for narrowing down their scope of legacy application migration. Organizations may also capitalize on it to shed light on migration rules hidden in the ancient legacy environment. COBOL to C# modernization solution uses an extensible and fully maintainable code base to develop functional equivalent target application. Migration from COBOL solution to C# applications involves language translation, analysis of all artifacts required for modernization, system acceptance testing, and database and data transfer. While it’s optional, companies could need improvements such as coding improvements, SOA integration, clean up, screen redesign, and cloud deployment.