Found this lovely and informative case study about Tableau Training from London Big Data firm ExistBi. Definitely a good read. Check them out at http://existbi.com/tableau-training

Science of Electroceuticals and Bioelectronics

Science of Electroceuticals and Bioelectronics

Electroceuticals 2016

Electroceuticals 2016

How Aldrich Bonnefin & Moore Used Enterprise Search to Change the Game

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Enterprise search has been proven to be an effective way to streamline and expedite the retrieval of data for companies spanning numerous industries. By creating a database for a specific audience, enterprise search helps organizations run more efficiently and is often a catalyst for increased productivity. To get a better sense of how this practice applies in a real world setting, let’s look at how Aldrich Bonnefin & Moore (AB&M) used it to better meet the needs of the financial institutions they serve.

 

Company Background

Based out of Irvine, California, AB&M are banking and finance attorneys who have significant expertise in business law, economics and legal trends. They use that knowledge to provide legal counsel to California financial institutions such as banks, credit unions and money service businesses and help them stay compliant. In order to serve their clients and provide them with accurate information, AB&M uses an abundance of data. And because new trends are constantly arising within their industry, it’s critical that they’re able to efficiently input new information while at the same time be able to retrieve existing data.

 

Implementing Enterprise Search

The platform that AB&M decided to use was Search Technologies, which caters to several other high-end companies such as Cisco, Johnson & Johnson and even the Library of Congress. Some of AB&M’s primary goals of implementing this platform were to:

• Improve the information search and retrieval process
• Make it so financial compliance information could be seamlessly accessed by their clients
• Transition to the future-proof enterprise search engine, Apache Solr
• Have a user-friendly interface

On top of this, they wanted to outsource the hosting of search so they could be more productive and not have to worry about the tedious process of maintaining a search infrastructure. That way, they could focus more of their attention on providing their clients with the best possible service and grow their core business.

 

How AB&M Benefited

First and foremost, the implementation of Apache Solr gave them a fast, efficient and reliable enterprise search engine that streamlined the search and retrieval process significantly. And because of the platform’s user-friendliness, it made the process hassle-free and allowed AB&M to accomplish more in less time. Also, they could access the platform via mobile devices for an added level of convenience.

In addition, they chose to use Search Technologies “Managed Search Services,” which meant that the company was responsible for the support and maintenance of the platform. In turn, this gave AB&M more freedom to place their attention on their business rather than managing their search infrastructure. The end result was that they were able to provide their clients with better service than ever before while at the same time simplifying operations.

This case study is a prime example of the multiple advantages that enterprise search can have for companies. Regardless of the industry, the right platform can greatly help companies improve the process of storing and retrieving data – and the positive impact can manifest itself in many different ways.

The post How Aldrich Bonnefin & Moore Used Enterprise Search to Change the Game appeared first on Maginfo.

MerchSource - Modular Closets

MerchSource - Modular Closets

5 Key Factors of Master Data Management

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Master data management (MDM) is where an organization manages data via a single point of reference is a growing trend, and more and more companies are getting on board. In fact, GlobeNewsWire reports “the global master data management market is expected to grow from $9,440.4 million in 2015 to $26,799.6 million by 2020, at a compound annual growth rate of 23.2% during the forecast period from 2015 to 2020.” But for MDM initiation to be successful and for organizations to truly reap the rewards, it relies upon five key factors.

1) Management and Staff Support

Perhaps most important of all is having the complete support of everyone involved with the process. Because of the often exhaustive nature of MDM initiation, long-term commitment and changes it can bring about within an organization, it’s crucial that everyone buys in and is willing to do whatever it takes to make the process a success. Otherwise, a lack of support can throw a serious wrench in things.

2) Quality Data

If master data is inaccurate, extraneous, contains duplicates or is generally of low quality, it basically defeats the purpose of implementing MDM. Not only can it be costly to an organization and reduce profitability, but it can put a damper on productivity as well. Consequently, it’s imperative that master data meets strict quality standards, which is why many companies utilize data profiling to assess the quality of data prior to data migration.

3) Efficient Data Integration

Besides upholding rigorous quality standards, the actual process of consolidating data and moving it to a master repository needs to be efficient. Whether it’s a single company creating a master repository between different departments or two companies combining data during a merger, the data integration process needs to be as streamlined as possible to minimize setbacks and prevent it from being overly time-consuming.

4) A Secure and Scalable Master Data Repository

In an age where cyber crime is an omnipresent threat, security should be a major priority for everyone involved with MDM. An organization should uphold scrupulous security standards throughout initial integration and continue to uphold them moving forward. And because there’s a good chance that a data model will require modifications at some point in the future, it’s wise to have a repository that’s flexible enough to accommodate those modifications.

5) Continual Quality Control

For MDM to be successful in the long run, it’s important for an organization to take quality control seriously. Developing a data quality assurance program is usually the most effective way to go about it – and different departments throughout an organization need to be on the same page to maintain consistency. Doing so should ensure that data remains of the highest possible quality for years to come.

The bottom line is that MDM can do wonders for a company and is likely to be a growing trend over the next five years plus. But in order for MDM to be a success, it’s contingent  upon these five main factors and a certain amount of persistence.

 

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Data Migration and Integration: A Marriage of Convenience

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Although data migration and data integration are two different processes, there’s a lot of overlap in terms of what they accomplish. When used together, they can bring about many positive benefits to an organization and simplify the way that data is retrieved.

Data migration, which is “the process of transferring data between data storage systems, data format or computer systems” is often used for server maintenance, upgrading servers or for relocating data. Data integration, which is “the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information” is typically used to consolidate and more efficiently manage information and is common when two businesses merge together and want to place all relevant information into a centralized database.

Now that we’ve covered the specific purposes of these two processes, let’s discuss how your data migration and integration work hand in hand when integrating technologies/data.

 

Ensuring High-Quality Data

For a business to run effectively, its systems and applications must contain high quality, trusted and accurate data. Otherwise, the time and money spent on acquiring and storing information will be squandered and operations can suffer. When data migration and integration are done in tandem, it becomes easier to manage and monitor data and ensure the highest possible quality. That’s because the two processes create a framework where data can be properly standardized, cleansed and ultimately reconciled.

 

An Example

Say two companies had a large volume of unstructured data from different sources. They could extract all of this information from existing databases and migrate it to a destination database. Along the way, the data can be standardized according to specific terms that will simplify the search process and prevent duplicate entries.

From there, data cleansing can be performed to correct incorrect on incomplete information, eliminate duplicates and resolve any other issues. Finally, data will be reconciled to ensure that the information in the destination database matches that of the original data sources.

 

Improved Collaboration

The end result of data migration and integration is streamlined collaboration between team members. When data is migrated to a destination database and quality rules are set in place, it means a higher level of consistency and that data is kept in optimal condition. In turn, employees who access the database will receive accurate information – and it’s easier for everyone to stay on the same page.

This reduces the potential for confusion and misunderstandings from arising, and employees can focus on what’s most important – core operations. That way, team members can collaborate with one another with greater and ease and at the same time provide customers/clients with the best possible experience.

Although data migration and integration projects can often be time-consuming and costly, the results are undeniable. When businesses utilize both of these processes, it creates a new, unified database of high-quality, standardized and accurate information. This ultimately makes everyone’s life easier and usually makes for a more productive workforce where profitability is maximized.

 

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