The Power of Jira

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Developers and development teams often have a lot on their plate when it comes to bringing software from conception to completion. When there are inefficiencies in the collaboration process or misunderstandings between team members, it can quickly thwart productivity and hurt overall progress. That’s where Jira Software comes in.

Jira is the “the #1 software development tool used by agile teams” and can help development teams get more done in less time with less friction. Here are some of the primary benefits of using Jira and some specific ways it can positively impact developers and development companies.

 

Conveniently Organize Tasks

When there are multiple team members working in unison on developing software, it’s critical that everyone stays on the same page. Each person needs to know what they’re responsible for, what’s in progress, what’s been completed, etc. Jira streamlines task management through the use of Scrum and Kanban boards so everyone has full visibility of what’s going on at any given time.

Whether team members are working from a single location or they’re scattered around the world, project workflow remains totally transparent. In turn, this drastically reduces misunderstandings and eliminates much of the chaos that can happen throughout the duration of a project.

 

Ensure Deadlines are Consistently Met

Often times, there little wiggle room in terms of deadlines – and they’re basically non-negotiable. Jira gets rid of much of the stress that can come with working with tight deadlines because of the simplicity of managing workflow. By tracking the overall progress of software development – and more specifically, knowing when code is being reviewed and what needs to be expedited, it’s easier to meet deadlines without a lot of hiccups along the way.

 

Streamline Feedback

One of the most essential aspects of collaboration during software development is ensuring that team members can seamlessly provide feedback to one another. Jira is perfect for streamlining feedback because team members can collaborate in real-time – and notifications can be delivered through any HipChat room. If someone suddenly discovers a bug, glitch or any other issue, it can be quickly resolved and everyone stays in the know.

 

Easy Access to Reporting

In today’s data driven world, success is often contingent upon having access to detailed analytics and comprehensive reporting. Not only will you want to know how team members’ time is being spent, you’ll want to know what their overall performance is like. Jira’s agile reporting provides you with over a dozen types of unique, real-time reports so you can monitor practically every aspect of development. All of which feature visual data to intuitively discern patterns and determine if any issues need to be addressed.

When you combine all of the features of Jira, the benefits are obvious. You can reduce or even eliminate inefficiencies, minimize much of the stress that comes along with software development, maximize productivity and know exactly how each team member is performing throughout each stage. As a result, this often translates into higher quality software and increased profitability.

 

The post The Power of Jira appeared first on Maginfo.

The Power of Jira

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Developers and development teams often have a lot on their plate when it comes to bringing software from conception to completion. When there are inefficiencies in the collaboration process or misunderstandings between team members, it can quickly thwart productivity and hurt overall progress. That’s where Jira Software comes in.

Jira is the “the #1 software development tool used by agile teams” and can help development teams get more done in less time with less friction. Here are some of the primary benefits of using Jira and some specific ways it can positively impact developers and development companies.

 

Conveniently Organize Tasks

When there are multiple team members working in unison on developing software, it’s critical that everyone stays on the same page. Each person needs to know what they’re responsible for, what’s in progress, what’s been completed, etc. Jira streamlines task management through the use of Scrum and Kanban boards so everyone has full visibility of what’s going on at any given time.

Whether team members are working from a single location or they’re scattered around the world, project workflow remains totally transparent. In turn, this drastically reduces misunderstandings and eliminates much of the chaos that can happen throughout the duration of a project.

 

Ensure Deadlines are Consistently Met

Often times, there little wiggle room in terms of deadlines – and they’re basically non-negotiable. Jira gets rid of much of the stress that can come with working with tight deadlines because of the simplicity of managing workflow. By tracking the overall progress of software development – and more specifically, knowing when code is being reviewed and what needs to be expedited, it’s easier to meet deadlines without a lot of hiccups along the way.

 

Streamline Feedback

One of the most essential aspects of collaboration during software development is ensuring that team members can seamlessly provide feedback to one another. Jira is perfect for streamlining feedback because team members can collaborate in real-time – and notifications can be delivered through any HipChat room. If someone suddenly discovers a bug, glitch or any other issue, it can be quickly resolved and everyone stays in the know.

 

Easy Access to Reporting

In today’s data driven world, success is often contingent upon having access to detailed analytics and comprehensive reporting. Not only will you want to know how team members’ time is being spent, you’ll want to know what their overall performance is like. Jira’s agile reporting provides you with over a dozen types of unique, real-time reports so you can monitor practically every aspect of development. All of which feature visual data to intuitively discern patterns and determine if any issues need to be addressed.

When you combine all of the features of Jira, the benefits are obvious. You can reduce or even eliminate inefficiencies, minimize much of the stress that comes along with software development, maximize productivity and know exactly how each team member is performing throughout each stage. As a result, this often translates into higher quality software and increased profitability.

 

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The Value of Search: Over CAD, BIM and Other Data Sources

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For many companies in the manufacturing and construction industries, CAD files, BIM files and similar data sources are integral to operations. Whether they’re being used for designing products on the small scale or for planning and constructing large-scale infrastructure, these are critical components that are used frequently. 

The problem is that as more and more files are accumulated, it creates vast volumes of data that individuals need to sift through. Without an effective methodology and system in place for locating and utilizing data, it can thwart productivity, reduce profitability and create some major headaches along the way.

 

The Pain of Locating Data

Research has found that “the average employee spends 9.3 hours a week searching for data and information, which is nearly 25 percent of a work week.” This can create a serious problem where employees end up spending an excessive amount of time searching for data rather than using it to accomplish tasks.

The bigger problem is that “corporate data will grow by 94 percent this year – and database systems by 97 percent.” This means that the volume of data that manufacturing and construction companies have will continue to grow exponentially in the future. On top of this, duplicate files can throw yet another wrench in things and create added confusion.

Without an effective means of storing, organizing and finding data, this can put a serious strain on operations and productivity is likely to suffer.

 

Enterprise Search as a Solution

Utilizing enterprise search software can be a lifesaver for companies in the manufacturing and construction industries and accomplishes three main things. First, it neatly stores and organizes data from CAD and BIM files in a database. Second, employees can quickly and conveniently crawl a company’s database to find the specific information they need within seconds. Third, employees can grab whatever files they’re looking for and use them to complete the project at hand.

When providing users with results, enterprise search takes several factors into account such as the user’s role and responsibility within a company, location, security clearance and the specific task they’re working on to deliver the exact content that’s needed.

 

The End Result

When you consider the intuitive, user-friendly nature of enterprise search and the way that it streamlines the process of locating data, it means one thing. Users are able to expedite the way in which they search for and retrieve data – and the amount of time each week spent searching for data is reduced significantly.

Instead of spending nearly a quarter of the workweek finding data, employees will spend a fraction of their time doing this. In turn, manufacturing and construction companies are able to reduce inefficiencies and can get the most from their employee resources.

 

The post The Value of Search: Over CAD, BIM and Other Data Sources appeared first on Maginfo.

The Value of Search: Over CAD, BIM and Other Data Sources

http://maginfo.com/wp-content/uploads/2015/12/AdobeStock_12202112-300×200.jpeg

For many companies in the manufacturing and construction industries, CAD files, BIM files and similar data sources are integral to operations. Whether they’re being used for designing products on the small scale or for planning and constructing large-scale infrastructure, these are critical components that are used frequently. 

The problem is that as more and more files are accumulated, it creates vast volumes of data that individuals need to sift through. Without an effective methodology and system in place for locating and utilizing data, it can thwart productivity, reduce profitability and create some major headaches along the way.

 

The Pain of Locating Data

Research has found that “the average employee spends 9.3 hours a week searching for data and information, which is nearly 25 percent of a work week.” This can create a serious problem where employees end up spending an excessive amount of time searching for data rather than using it to accomplish tasks.

The bigger problem is that “corporate data will grow by 94 percent this year – and database systems by 97 percent.” This means that the volume of data that manufacturing and construction companies have will continue to grow exponentially in the future. On top of this, duplicate files can throw yet another wrench in things and create added confusion.

Without an effective means of storing, organizing and finding data, this can put a serious strain on operations and productivity is likely to suffer.

 

Enterprise Search as a Solution

Utilizing enterprise search software can be a lifesaver for companies in the manufacturing and construction industries and accomplishes three main things. First, it neatly stores and organizes data from CAD and BIM files in a database. Second, employees can quickly and conveniently crawl a company’s database to find the specific information they need within seconds. Third, employees can grab whatever files they’re looking for and use them to complete the project at hand.

When providing users with results, enterprise search takes several factors into account such as the user’s role and responsibility within a company, location, security clearance and the specific task they’re working on to deliver the exact content that’s needed.

 

The End Result

When you consider the intuitive, user-friendly nature of enterprise search and the way that it streamlines the process of locating data, it means one thing. Users are able to expedite the way in which they search for and retrieve data – and the amount of time each week spent searching for data is reduced significantly.

Instead of spending nearly a quarter of the workweek finding data, employees will spend a fraction of their time doing this. In turn, manufacturing and construction companies are able to reduce inefficiencies and can get the most from their employee resources.

 

The post The Value of Search: Over CAD, BIM and Other Data Sources appeared first on Maginfo.

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Technical Estimations: The Love-Hate Relationship Story

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Estimations Are Hard

 

For software developers, creating a meaningful technical estimate is one of the most difficult things you will ever do on a regular basis. A good estimate must take into account so many (often vague) variables like the technologies being used, the scope of work and even the client themselves. On top of that,  any attempt to propose a solution based on a development-level understanding of a client’s needs often differs from what the client actually thinks they want (or need) in the first place, especially when they don’t have a technical background.

How developers create and present technical estimations also has consequences, both good and bad. Overestimation can scare a client and lead them to place a project on hold, or even find another firm to do the same job. Underestimation may initially cause the client to accept your proposal, but will put the integrity of your team and business in jeopardy. Unfortunately, even the smallest miscalculation can lead to confusion and with so much at stake, no wonder developers cringe at the thought of putting together a technical estimate. Now while an estimate is rarely perfect, there are still a number of ways to make estimates as accurate as possible.


 

Reaching A Common Understanding:

Without exception, when your dev team begins the estimation process, they’re naturally hesitant to put together a proposal broken down by days and hours on a piece of paper. Turning a request for proposal (RFP) into a meaningful estimate takes time, research and analysis; questions are also always bound to arise ranging from intended functionality to the true meaning of user stories. But this is good, developers should be asking questions!

Considering that most clients are not “technical individuals”, what I’ve noticed is that many clients view an estimate as an unchangeable oath written in blood. In contrast, developers tend to view an estimate as nothing more than the definition of the word itself, a rough calculation based on what is known at the time. That said, developers often change estimates as new information and requirements are discovered, often to the dismay of the client. While a software engineer may understand that even the smallest feature or change in functionality can impact the scope of an entire project, most product owners simply don’t see it that way.

Presentation Is Key: Knowing your Audience

Being able to distinguish between whether your client wants a rough order of magnitude (ROM) or a true request for proposal (RFP) can save a lot of time otherwise spent on unnecessary work. Depending on the project, various members of your team may also bring a different point of view (POV) that will impact the estimation approach being used. Believe me, product managers, developers, quality assurance and sales all see things a little differently. While this can be a good thing, having an understanding of your audience is arguably the biggest factor impacting how a technical estimate is made, presented and what level of accuracy is needed –  knowing whether your estimate is going to another engineer or to your average joe makes a huge difference here.

In the past, we’ve experimented a bit with different types of technical estimates and have found that other dev teams, technical individuals or companies requiring SAAS solution want the most comprehensive estimate you can give. On top of a comprehensive estimate, they want a list of technologies, the justification for these technologies, and an hour-by-hour breakdown on every individual task – these proposals took a stupid amount of time to create, but that’s what the client wanted, that’s who our audience was. On the flip side, some of our clients were small businesses, they did not have a technical background and simply wanted a web or mobile application. The estimation approach used for them consisted of 1-3 pages where we reiterate their needs and provided a summary of the sprints each broken down by hours. The difference in terms of time spent between these two types of estimates is sometimes multiple weeks or even months.

Margin for Error:

Developers never make mistakes and everything is done correctly the first time around.. (said no one ever!). Even with the absolute best dev teams, not including a margin for error in a technical estimate can be disastrous. In a sense, if you’re not including some wiggle-room for reworks, you’re underestimating the total effort needed for completing your deliverables. When possible, give estimates as a range when it makes sense and be more precise when a range is not needed. Mockups are a great example here because clients tend to have their own idea of what looks good and rework is almost always necessary. However, once mockups are approved by the client, the margin for error contributing to rework is not nearly as high – taking this into account when making an estimate can significantly improve accuracy.

 

Example:

Phase 1.
Mockups & Design: 2-6 days

Description: At this step, Maginfo will prepare mockups and designs for the project. Upon completion, approval of mock-ups will be needed prior to moving to the next step of development.

 

Phase 2.
Development: 6 days

Description: At this step, Maginfo will implement the project based on approved mockups and specifications.

The Benefit of a Good Estimate:

For product owners and clients specifically, having an itemized breakdown of functionality at the granular level makes it easier to weigh and prioritize different aspects of the project and even put “nice-to-have” features on the backburner… At the end of the day, it’s way easier just to sit down, write some code and bill for the hours, but even developers understand that this is unrealistic from a client perspective. Nevertheless, reaching a mutual understanding between the client and the dev team regarding the scope of work should be the #1 priority before kickstarting any project.

 

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Technical Estimations: The Love-Hate Relationship Story

http://maginfo.com/wp-content/uploads/2015/11/AdobeStock_64170691-300×205.jpeg

Estimations Are Hard

 

For software developers, creating a meaningful technical estimate is one of the most difficult things you will ever do on a regular basis. A good estimate must take into account so many (often vague) variables like the technologies being used, the scope of work and even the client themselves. On top of that,  any attempt to propose a solution based on a development-level understanding of a client’s needs often differs from what the client actually thinks they want (or need) in the first place, especially when they don’t have a technical background.

How developers create and present technical estimations also has consequences, both good and bad. Overestimation can scare a client and lead them to place a project on hold, or even find another firm to do the same job. Underestimation may initially cause the client to accept your proposal, but will put the integrity of your team and business in jeopardy. Unfortunately, even the smallest miscalculation can lead to confusion and with so much at stake, no wonder developers cringe at the thought of putting together a technical estimate. Now while an estimate is rarely perfect, there are still a number of ways to make estimates as accurate as possible.


 

Reaching A Common Understanding:

Without exception, when your dev team begins the estimation process, they’re naturally hesitant to put together a proposal broken down by days and hours on a piece of paper. Turning a request for proposal (RFP) into a meaningful estimate takes time, research and analysis; questions are also always bound to arise ranging from intended functionality to the true meaning of user stories. But this is good, developers should be asking questions!

Considering that most clients are not “technical individuals”, what I’ve noticed is that many clients view an estimate as an unchangeable oath written in blood. In contrast, developers tend to view an estimate as nothing more than the definition of the word itself, a rough calculation based on what is known at the time. That said, developers often change estimates as new information and requirements are discovered, often to the dismay of the client. While a software engineer may understand that even the smallest feature or change in functionality can impact the scope of an entire project, most product owners simply don’t see it that way.

Presentation Is Key: Knowing your Audience

Being able to distinguish between whether your client wants a rough order of magnitude (ROM) or a true request for proposal (RFP) can save a lot of time otherwise spent on unnecessary work. Depending on the project, various members of your team may also bring a different point of view (POV) that will impact the estimation approach being used. Believe me, product managers, developers, quality assurance and sales all see things a little differently. While this can be a good thing, having an understanding of your audience is arguably the biggest factor impacting how a technical estimate is made, presented and what level of accuracy is needed –  knowing whether your estimate is going to another engineer or to your average joe makes a huge difference here.

In the past, we’ve experimented a bit with different types of technical estimates and have found that other dev teams, technical individuals or companies requiring SAAS solution want the most comprehensive estimate you can give. On top of a comprehensive estimate, they want a list of technologies, the justification for these technologies, and an hour-by-hour breakdown on every individual task – these proposals took a stupid amount of time to create, but that’s what the client wanted, that’s who our audience was. On the flip side, some of our clients were small businesses, they did not have a technical background and simply wanted a web or mobile application. The estimation approach used for them consisted of 1-3 pages where we reiterate their needs and provided a summary of the sprints each broken down by hours. The difference in terms of time spent between these two types of estimates is sometimes multiple weeks or even months.

Margin for Error:

Developers never make mistakes and everything is done correctly the first time around.. (said no one ever!). Even with the absolute best dev teams, not including a margin for error in a technical estimate can be disastrous. In a sense, if you’re not including some wiggle-room for reworks, you’re underestimating the total effort needed for completing your deliverables. When possible, give estimates as a range when it makes sense and be more precise when a range is not needed. Mockups are a great example here because clients tend to have their own idea of what looks good and rework is almost always necessary. However, once mockups are approved by the client, the margin for error contributing to rework is not nearly as high – taking this into account when making an estimate can significantly improve accuracy.

 

Example:

Phase 1.
Mockups & Design: 2-6 days

Description: At this step, Maginfo will prepare mockups and designs for the project. Upon completion, approval of mock-ups will be needed prior to moving to the next step of development.

 

Phase 2.
Development: 6 days

Description: At this step, Maginfo will implement the project based on approved mockups and specifications.

The Benefit of a Good Estimate:

For product owners and clients specifically, having an itemized breakdown of functionality at the granular level makes it easier to weigh and prioritize different aspects of the project and even put “nice-to-have” features on the backburner… At the end of the day, it’s way easier just to sit down, write some code and bill for the hours, but even developers understand that this is unrealistic from a client perspective. Nevertheless, reaching a mutual understanding between the client and the dev team regarding the scope of work should be the #1 priority before kickstarting any project.

 

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Big Data: Reading Information from Sensors and Data Analytics

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As technology becomes increasingly sophisticated, big data is being utilized by businesses across countless industries. The tools that are used are also becoming more advanced – and now sensors are playing a bigger role in data analytics. Defined as “the statistical analysis of data that is created by wired or wireless sensors,” sensor analytics can be used to help companies run more efficiently, better understand consumer needs, improve targeting efforts and much more.

The only problem is, how do you effectively read sensor data and interpret it in a way that’s useful? Here are some techniques.

 

Spotting Anomalies

Identifying events that don’t conform to typical patterns can be highly important in several circumstances. One example pertains to online security where an anomaly could indicate a network intrusion. This would help a company detect a security threat and disarm a situation before it becomes serious. Another example pertains to the healthcare industry where an anomaly in a medical diagnosis would inform doctors or nurses so they could quickly address the issue and potentially save the life of a patient.

Sensor data makes it possible to identify atypical events much quicker and more conveniently than in the past. By looking at a high volume of data where everything is more or less consistent, it becomes easy to spot anomalies, which can be useful in many ways.

 

Trends Detection

A big part of staying competitive in business is being able to spot trends and stay on the cutting edge. Wired or wireless sensors help streamline this process because they can be used to generate large volumes of data, which make it possible to spot overarching trends that might otherwise be difficult. In order to use sensor data in a practical way, businesses can examine data on the large scale and search for noticeable trends that could be indicators of patterns that could influence the approach they choose to take to operations.

 

Visualization

There’s no doubt that humans are inherently visual creatures – and one of the best ways to make use of a large body of information is to utilize data visualization tools. While visualization can be used in numerous ways, one of the most valuable is for helping businesses better understand customer behavior and spot opportunities. Data Informed offers a good example:

“Business leaders for a supermarket chain can use data visualization to see that not only are customers spending more in its stores as macro-economics improve, but they are increasingly interested in purchasing ready-made foods.” When it comes to examining data that would otherwise be difficult to interpret, visualization makes it significantly easier and more intuitive.

By understanding how to effectively read information from sensors and data analytics, it minimizes any guesswork. In turn, businesses can take abstract information and transform it into something that’s much more concrete so that it can be used in a practical way. And when you consider the long-term implications, this can have a dramatic impact on operations and put companies in a better position to succeed.

 

The post Big Data: Reading Information from Sensors and Data Analytics appeared first on Maginfo.

Big Data: Reading Information from Sensors and Data Analytics

http://maginfo.com/wp-content/uploads/2015/11/AdobeStock_77007744-300×225.jpeg

As technology becomes increasingly sophisticated, big data is being utilized by businesses across countless industries. The tools that are used are also becoming more advanced – and now sensors are playing a bigger role in data analytics. Defined as “the statistical analysis of data that is created by wired or wireless sensors,” sensor analytics can be used to help companies run more efficiently, better understand consumer needs, improve targeting efforts and much more.

The only problem is, how do you effectively read sensor data and interpret it in a way that’s useful? Here are some techniques.

 

Spotting Anomalies

Identifying events that don’t conform to typical patterns can be highly important in several circumstances. One example pertains to online security where an anomaly could indicate a network intrusion. This would help a company detect a security threat and disarm a situation before it becomes serious. Another example pertains to the healthcare industry where an anomaly in a medical diagnosis would inform doctors or nurses so they could quickly address the issue and potentially save the life of a patient.

Sensor data makes it possible to identify atypical events much quicker and more conveniently than in the past. By looking at a high volume of data where everything is more or less consistent, it becomes easy to spot anomalies, which can be useful in many ways.

 

Trends Detection

A big part of staying competitive in business is being able to spot trends and stay on the cutting edge. Wired or wireless sensors help streamline this process because they can be used to generate large volumes of data, which make it possible to spot overarching trends that might otherwise be difficult. In order to use sensor data in a practical way, businesses can examine data on the large scale and search for noticeable trends that could be indicators of patterns that could influence the approach they choose to take to operations.

 

Visualization

There’s no doubt that humans are inherently visual creatures – and one of the best ways to make use of a large body of information is to utilize data visualization tools. While visualization can be used in numerous ways, one of the most valuable is for helping businesses better understand customer behavior and spot opportunities. Data Informed offers a good example:

“Business leaders for a supermarket chain can use data visualization to see that not only are customers spending more in its stores as macro-economics improve, but they are increasingly interested in purchasing ready-made foods.” When it comes to examining data that would otherwise be difficult to interpret, visualization makes it significantly easier and more intuitive.

By understanding how to effectively read information from sensors and data analytics, it minimizes any guesswork. In turn, businesses can take abstract information and transform it into something that’s much more concrete so that it can be used in a practical way. And when you consider the long-term implications, this can have a dramatic impact on operations and put companies in a better position to succeed.

 

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Predictive Analytics with Big Data

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Big data fuels predictive analytics because without adequate data it’s difficult for organizations to make accurate predictions about future events. Generally speaking, there is a correlation between a large volume of data and a high degree of accuracy and a smaller volume of data and a lower degree of accuracy. Of course this is assuming that an organization is utilizing best practices and effective data management techniques.

When this is the case, the larger the volume of data, the better – and as an organization accumulates more data, this allows it to make more accurate predictions and create actionable intelligence for the future.

 

When is Data Considered Big?

The term “big data” is often used in a broad sense and somewhat subjective. According to a popular big data study in 2011 by McKinsey & Company, it’s defined as “data sets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Some would say that once data gets in the gigabyte range that it’s considered big, while others would say that it’s petabytes. Regardless of what the precise definition may be, the more data an organization has, the better its decision-making typically becomes.  

 

Examples of Utilizing Predictive Analytics with Big Data

By having access to a large body of data such as previous customer purchases, buying patterns, etc., a business could use both stored and real-time information to its advantage when sending out promotions. For example, if a business knows that a group of customers have purchased a particular product, they could send out promotional materials featuring a similar product in which the customers would be highly likely to buy.

Another example pertains to website exploration. For instance, if a customer looked at site pages featuring a particular product or service, the company could provide a unique website experience that’s tailored to the customer’s specific interests.   

While having only a small body of data would probably allow some degree of accuracy in terms of gauging what customers are looking for, having a much large body of data should give the company a significantly higher level of certainty. The point is that big data is usually advantageous over small data because it helps organizations get the most out of predictive analytics.

 

Accuracy is Contingent Upon Data Quality

On a side note, it’s important mention that there are limitations to big data – and there’s a certain point where there’s so much data that it’s actually counterproductive and a hindrance. For big data to be effective, it’s critical that an organization utilizes some form of data management where it becomes properly organized and obsolete information is deleted once it’s no longer useful. Basically, big data must be “tamed” and structured in a way that it ensures that the information found via predictive analytics is legitimately helpful.

The bottom line is that predictive analytics used in conjunction with big data enables organizations to make sound decisions. And when best practices are utilized, this gives an organization a significant edge when predicting future events.  

The post Predictive Analytics with Big Data appeared first on Maginfo.

Predictive Analytics with Big Data

http://maginfo.com/wp-content/uploads/2015/10/AdobeStock_58475432-300×200.jpeg

Big data fuels predictive analytics because without adequate data it’s difficult for organizations to make accurate predictions about future events. Generally speaking, there is a correlation between a large volume of data and a high degree of accuracy and a smaller volume of data and a lower degree of accuracy. Of course this is assuming that an organization is utilizing best practices and effective data management techniques.

When this is the case, the larger the volume of data, the better – and as an organization accumulates more data, this allows it to make more accurate predictions and create actionable intelligence for the future.

 

When is Data Considered Big?

The term “big data” is often used in a broad sense and somewhat subjective. According to a popular big data study in 2011 by McKinsey & Company, it’s defined as “data sets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Some would say that once data gets in the gigabyte range that it’s considered big, while others would say that it’s petabytes. Regardless of what the precise definition may be, the more data an organization has, the better its decision-making typically becomes.  

 

Examples of Utilizing Predictive Analytics with Big Data

By having access to a large body of data such as previous customer purchases, buying patterns, etc., a business could use both stored and real-time information to its advantage when sending out promotions. For example, if a business knows that a group of customers have purchased a particular product, they could send out promotional materials featuring a similar product in which the customers would be highly likely to buy.

Another example pertains to website exploration. For instance, if a customer looked at site pages featuring a particular product or service, the company could provide a unique website experience that’s tailored to the customer’s specific interests.   

While having only a small body of data would probably allow some degree of accuracy in terms of gauging what customers are looking for, having a much large body of data should give the company a significantly higher level of certainty. The point is that big data is usually advantageous over small data because it helps organizations get the most out of predictive analytics.

 

Accuracy is Contingent Upon Data Quality

On a side note, it’s important mention that there are limitations to big data – and there’s a certain point where there’s so much data that it’s actually counterproductive and a hindrance. For big data to be effective, it’s critical that an organization utilizes some form of data management where it becomes properly organized and obsolete information is deleted once it’s no longer useful. Basically, big data must be “tamed” and structured in a way that it ensures that the information found via predictive analytics is legitimately helpful.

The bottom line is that predictive analytics used in conjunction with big data enables organizations to make sound decisions. And when best practices are utilized, this gives an organization a significant edge when predicting future events.  

The post Predictive Analytics with Big Data appeared first on Maginfo.

from The Mag Info https://themaginfo.wordpress.com/2015/11/05/predictive-analytics-with-big-data/
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Learn About Master Data Management (MDM) and How it Transforms Business Data into Value Drivers

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Master data management (MDM) is defined as “a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference” is one way that businesses make big data more efficient. By using a master file, companies can improve both the quality of information, and it can serve as a value driver. Let’s now look at some specific ways this is accomplished.

 

Consistency Equals Better Efficiency  

 

In order for big data to truly do a business any good and have any tangible results, there needs to be consistency. For example, if customer data is fragmented, inaccurate or there are duplicates, it’s going to make it more difficult to effectively upsell or cross-sell –not to mention it can throw a wrench in the supply chain. But when MDM is integrated, it tends to result in more consistent data and more cohesion. In turn, managers and employees can utilize business applications and software with more efficiency, and they’re less apt to make needless mistakes.  

 

In the case of upselling and cross-selling, MDM would increase the likelihood that customers would receive promotions for products/services that they are genuinely interested in, and a company could avoid sending multiple promotions to a single customer. When it comes to supply chain management, MDM can help a company improve distribution and the flow of goods so that it could better meet demand.

 

Increased Profitability

 

When you look at the long-term impact of this added efficiency, it means one thing – a business often becomes more profitable. Here are two examples to prove how:

 

1. A business that uses MDM to govern its data could become better skilled at upselling and cross-selling and would likely see a higher conversion rate and get the most out of promotions. They would also be able to build stronger relationships with their existing customers, which would lead to repeat sales and more word of mouth advertising. In turn, the business would see a better ROI and become more profitable in general.

 

1. A company that uses MDM for handling the data from its supply chain management would be able distribute products more quickly and with less friction than it would if it were lacking a solid data management system. This means that it would be able to keep up with customer demand more easily and be poised to capitalize on its most profitable products while they’re popular. As a result, it could streamline the distribution process and reduce supply chain costs, which again means more profitability.

 

And as the amount of data that a business accumulates continues to grow, it becomes even more essential to incorporate MDM. That way it’s better equipped to establish an effective information infrastructure that keeps nearly all aspects of operations running smoothly. So when you look at the big picture, MDM allows a business to function like a well-oiled machine while simultaneously improving the customer experience for a win-win situation.

The post Learn About Master Data Management (MDM) and How it Transforms Business Data into Value Drivers appeared first on Maginfo.

from The Mag Info https://themaginfo.wordpress.com/2015/10/15/learn-about-master-data-management-mdm-and-how-it-transforms-business-data-into-value-drivers/
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Learn About Master Data Management (MDM) and How it Transforms Business Data into Value Drivers

http://maginfo.com/wp-content/uploads/2015/10/AdobeStock_86395434_WM-300×242.jpeg

Master data management (MDM) is defined as “a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference” is one way that businesses make big data more efficient. By using a master file, companies can improve both the quality of information, and it can serve as a value driver. Let’s now look at some specific ways this is accomplished.

 

Consistency Equals Better Efficiency  

 

In order for big data to truly do a business any good and have any tangible results, there needs to be consistency. For example, if customer data is fragmented, inaccurate or there are duplicates, it’s going to make it more difficult to effectively upsell or cross-sell –not to mention it can throw a wrench in the supply chain. But when MDM is integrated, it tends to result in more consistent data and more cohesion. In turn, managers and employees can utilize business applications and software with more efficiency, and they’re less apt to make needless mistakes.  

 

In the case of upselling and cross-selling, MDM would increase the likelihood that customers would receive promotions for products/services that they are genuinely interested in, and a company could avoid sending multiple promotions to a single customer. When it comes to supply chain management, MDM can help a company improve distribution and the flow of goods so that it could better meet demand.

 

Increased Profitability

 

When you look at the long-term impact of this added efficiency, it means one thing – a business often becomes more profitable. Here are two examples to prove how:

 

1. A business that uses MDM to govern its data could become better skilled at upselling and cross-selling and would likely see a higher conversion rate and get the most out of promotions. They would also be able to build stronger relationships with their existing customers, which would lead to repeat sales and more word of mouth advertising. In turn, the business would see a better ROI and become more profitable in general.

 

1. A company that uses MDM for handling the data from its supply chain management would be able distribute products more quickly and with less friction than it would if it were lacking a solid data management system. This means that it would be able to keep up with customer demand more easily and be poised to capitalize on its most profitable products while they’re popular. As a result, it could streamline the distribution process and reduce supply chain costs, which again means more profitability.

 

And as the amount of data that a business accumulates continues to grow, it becomes even more essential to incorporate MDM. That way it’s better equipped to establish an effective information infrastructure that keeps nearly all aspects of operations running smoothly. So when you look at the big picture, MDM allows a business to function like a well-oiled machine while simultaneously improving the customer experience for a win-win situation.

The post Learn About Master Data Management (MDM) and How it Transforms Business Data into Value Drivers appeared first on Maginfo.

What is Enterprise Search?

What is Enterprise Search?

Watch video on YouTube here: http://youtu.be/4pF2pAmEdEg
via Mag Info | Turning Big Ideas Into Bigger Opportunities

from The Mag Info https://themaginfo.wordpress.com/2015/10/13/what-is-enterprise-search/
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What is Enterprise Search?

What is Enterprise Search?