Glossary of Business Intelligence & Financial Reporting Terminology

Actionable Reporting

insightsoftware — Wed, 23 Sep 2015 16:49:33 +0000

Making a report or report features actionable can mean different things:

Setting up automated business alerts, to inform decision-makers of critical pieces of information so that immediate action can be taken
Scheduling and delivering reports automatically, so that users can always be armed with critical information without having to run the report themselves
Automated processes and integration with other business applications, so that, for instance, action taken within the BI application (e.g. placing an order) can be written directly into the database

In all cases, the goal of actionable reports is to set up processes to make information jump out to the user and to let the user act on it without leaving the application or having to record the action in another interface.

Benefits of Actionable Reporting

The main benefit of actionable reporting is to make information dynamically reach its purpose without additional action on the part of the user.

When the purpose is knowledge of critical items, automated report scheduling lets data and information reach users effortlessly, so that they can focus on decision-making rather than on running reports
When the purpose is acting on key information, automated business alerts let decision-makers know when a critical situation crosses a pre-defined threshold, so that action can be taken without need for any further analysis
When the purpose is acting on the data, automated processes and integration with other business applications lets users take action from within the report, saving time and effort

The post Actionable Reporting appeared first on insightsoftware.

Active Directory

insightsoftware — Thu, 14 Jan 2016 20:00:02 +0000

Active Directory is a Microsoft-developed directory service that stores information about Windows networks. Stored network components or objects may include organizations, sites, systems, users, and shares. When integrated with business intelligence platforms like Logi Info, Active Directory and other security frameworks may be used to authenticate users via single-sign on.

The post Active Directory appeared first on insightsoftware.

Ad Hoc Reporting

insightsoftware — Wed, 23 Sep 2015 16:12:52 +0000

Ad-hoc reporting is a model of business intelligence (BI) in which reports are built and distributed by nontechnical business intelligence users. In other words, with ad-hoc reporting, all the technical user does is set up the BI solution, connect it to the data-sources, establish security parameters and determine which objects end-users can see. From that point on, the actual reports are created by business end-users.

Ad-hoc is Latin for “as the occasion requires.” This means that with this BI model, users can use their reporting and analysis solution to answer their business questions “as the occasion requires,” without having to request queries from IT. Naturally, ad-hoc reports can be and look as simple as a one page data table or as complex and rich as interactive tabular or cross-tab reports with drill-down and visualization features–or present themselves in the form of dashboards, heat maps, or other more advanced forms.

This depends in large part on a) the type of ad-hoc solution employed, b) the needs of the end-user and c) the user’s confidence with the solution.

Want to see ad-hoc reporting in action? Check out this on-demand demo of Logi’s dashboarding and reporting solution.

Ad-hoc reporting stands in contrast with managed reporting, in which it is the technical user–the report developer–who creates and distributes the report.

The Goal of Ad-hoc Reporting & Analysis

Ad-hoc reporting’s goal is to empower end-users to ask their own questions of company data, without burdening IT with the task of creating a myriad of reports to serve different functions and purposes. Ad-hoc analysis therefore makes the most sense when a large number of end-users need to see, understand, and act on data more or less independently, while still being on the same page as far as which set of numbers they look at.

For example, a company with a large outside-sales force would be the perfect fit for ad-hoc reporting. Each sales rep can set up his own report for his territory, showing performance against sales goals, orders taken, number of visits to each client, etc., in a format that makes the most sense to him. And just as importantly, the numbers used are pulled from the same data sources as the rest of the company, thereby promoting consistency and minimizing surprises at the end of the quarter.

A good-quality, Web-based ad-hoc reporting solution greatly enhances the benefits of the ad-hoc reporting model for the company adopting it.

The Benefits of Web-based Ad-hoc Reporting & Analysis

Get critical information to the right people at the right time – Self-service results plus automatic scheduling/delivery of information let you facilitate timely decision making. Users get the information they need when they need it to answer critical, real-time questions.
Flexibility for constantly changing environments – Business needs to evolve. Answers to changing business questions become more critical. It’s impossible to predict what questions and answers users may need in the future.
Saves training costs and time – Streamlines users’ access to critical information. Easy-to-use wizards allow users to get up and running quickly, requiring less time to learn the application and providing clear guidance and saving time to build reports.
Encourages collaboration and information sharing – Users can easily create, organize, publish and make reports available to other users via the Web for on-demand viewing.
Reduces IT workload – The Web-based reporting application itself can be deployed quickly for widespread availability to end-users. Once deployed, it empowers users to build the reports themselves anytime they need the information. No waiting for IT report developers to build them.

What to Look For in a Good Ad-hoc Reporting Solution

A good ad-hoc reporting solution should–like all BI applications–be squarely aimed at the achievement of the company’s strategy. The key here is to identify what each end-user’s strategic function is within the organization, and ensure that the ad-hoc reporting solution is optimized to make that function easier and more effective, while not offsetting benefits by being too costly.

To do so, a good reporting solution will offer the following characteristics:

Being easy to use. If it is or even appears to be complicated, many end-users will be turned off and user adoption will suffer. For this reason, some of the better ad-hoc reporting solutions available today offer a basic set of intuitive features that are wizard-driven and will look easy even to the proverbial “non-computer person,” while also offering more advanced sets of tools for the user who feels confident.
Being robust. Assuming that adoption is not an issue (see previous point), the ad-hoc solution should offer end-users what they need to see, understand and act upon their data. Far from being a more hi-tech version of Excel, it should offer interactive features like ad-hoc dashboards, drill-down and drill-through, advanced sorting and filtering, rich visualization tools like heat maps, charts and graphs, etc.
Being Web-based. For it to be truly useful, a BI solution (including ad-hoc reporting) should run on the Internet. Apart from offering the familiar navigability with which we are all familiar, a Web-based solution is available from virtually anywhere and on any device sporting Internet connection. Another benefit of a Web-based ad-hoc analysis solution is that the system administrator won’t have to set it up individually on every user’s machine: installing it on the server is enough, and all the users need to access it is a simple URL.
Being easy to set up. Today’s better Web-based ad-hoc reporting solutions are data-source neutral, meaning that they can connect practically out of the box to most of today’s commonly-used data-sources, including databases, Web-services, flat files, etc. This saves the IT department the burden of creating complex metadata structures as the underlying layer, which is time-consuming, cumbersome and expensive.
Having a server-based licensing with no per-user fees. If the benefit of ad-hoc reporting is that of empowering end-users, it should not come with a “user-tax” in the form of per-seat licensing.

The post Ad Hoc Reporting appeared first on insightsoftware.

Advanced Analytics

insightsoftware — Mon, 04 Jan 2016 22:09:55 +0000

Advanced analytics is a desirable feature that may be embedded in software applications to provide deeper insights into trends. Applications utilizing this feature provide a unique value proposition by developing advanced (and often proprietary) statistical models and making advanced analytics easily accessible in the users’ analysis.

In addition to delivering a base level of analytics in the application (typically referred to as table stakes), software providers may differentiate themselves by adding more sophisticated self-service functionality and advanced analytics.

The post Advanced Analytics appeared first on insightsoftware.

Agile BI

insightsoftware — Thu, 14 Jan 2016 20:01:39 +0000

Agile BI (business intelligence) is a flexible and scalable architecture that embraces rapid, iterative development and the commoditization of data storage. This agile architecture allows organizations to quickly adapt to changing business requirements while reducing total cost of ownership. As a result, companies are able to rapidly and affordably respond to changing market conditions. In short, agile business intelligence delivers on the IT mantra of “do more with less.”

Agile BI’s flexibility, scalability, and affordability are revolutionizing the way companies use BI in the same way cloud computing transformed the way we look at data storage. An agile architecture is foundational to delivering next-generation BI capabilities like self-service, mobile, and big data. As companies increasingly demand BI solutions that deliver competitive insights in an accessible manner to a broader audience, the adoption of agile BI is a necessity, not an option.

The post Agile BI appeared first on insightsoftware.

Amazon Redshift

insightsoftware — Mon, 28 Sep 2015 15:27:15 +0000

Amazon Redshift is a data warehouse product built by Amazon. Available on Amazon Web Services, Redshift is able to handle analytics workloads on large scale datasets and is searchable using traditional BI applications and SQL methods.

The product manages setting up, operating, and scaling data warehouses. It automatically monitors notes and drives to help recover from failures, provision the infrastructure capacity, and operate administrative tasks, such as back-ups and patching.

Amazon Redshift’s performance can reach at least ten times higher than other databases for data warehousing and analytics workloads. A few of its features include:

Columnar data storage: Unlike other databases which use a series of rows, this feature is ideal for data warehousing and analytics because the system requires fewer I/Os, leading to an improved query performance.
Advanced compression: With its columnar-based data storage, Amazon Redshift automatically samples data and selects the most appropriate compression scheme when loading data into an empty table.
Massively Parallel Processing (MPP): Amazon Redshift automatically distributes data and query across nodes, which allows for faster query performance as the data warehouse grows.

Advantages of Amazon Redshift:

The ability to access large databases in a low-cost and timely manner
The ability to handle databases larger than a petabyte
The ability to quickly return results with a variety of resources
The low cost can save a lot of money, compared to the cost of using your own hardware and software

The post Amazon Redshift appeared first on insightsoftware.

Analysis Grid

insightsoftware — Mon, 28 Sep 2015 16:19:40 +0000

Use the Analysis Grid to answer important questions from your data.

Powerful, Flexible Data Analysis

The Analysis Grid is one of the most powerful–yet easy to use–data-analysis tools available today. It is a feature that allows developers to create a grid of data for business users to analyze and query in multiple and powerful ways. It is a managed reporting feature giving end users virtual ad hoc capability.

Although Web-based, the Analysis Grid offers the interactivity of a dedicated desktop application. It is composed of three main parts:

The data grid itself, i.e. a table of data to be analyzed
A number of action buttons at the top, allowing the user to perform actions such as:
- Create new columns with custom calculations
- Hide and move columns
- Sort columns
- Filter data
- Add groupings
- Perform aggregations
- Add charts
- Add cross-tab views
- Control paging
And the ability to export the grid to Excel, CSV or PDF format

The Benefits of the Analysis Grid

After it’s mastered, which is generally a quick and intuitive process, the Analysis Grid can yield a tremendous amount of answers to business questions. Among the many benefits of this analysis feature, here are the most salient:

Giving end-users virtual ad-hoc analysis capability on their data
Allowing end-users to create custom columns and perform complex calculations at the click of a mouse
Making it easy to perform what-if analysis through features like filtering
Making data-presentation impactful through visualization features including heat maps
Letting the user save his analysis grid report and reuse it whenever needed
Letting the user export his findings in other formats such as Excel, CSV and PDF

The post Analysis Grid appeared first on insightsoftware.

Analysis is Turning Data into Actionable Information

insightsoftware — Mon, 28 Sep 2015 16:37:42 +0000

Analyzing data, in general, assumes that the data has already been presented, or “reported” on–in the strict definition of the word. Analyzing literally means “taking apart,” i.e. sifting through something, breaking it down in its components to better understand it. Analysis in business intelligence is therefore the art of understanding data by “taking it apart” and asking it relevant questions.

Or, put an even better way, reporting presents data; analysis turns data into information. Information that, to be useful, can be then acted upon in the interest of the company’s strategy.

We can look at analysis as the simple act of asking your data questions. Take a table of data, for example, showing you a column of sales reps’ names and another column displaying total orders taken. The data is neutral. You can’t immediately make business sense of this simple table, unless you ask it questions. Now, ask the table “who has taken the highest amount of orders?” by sorting the second column, descending. Now the data has turned into information. A simple sort has been your way to ask your data a question, and you are therefore armed with the piece of information that (say), Jones is your top-performing sales rep.

Naturally, analysis can be much more complex than this. It can involve looking at your data from multiple dimensions (OLAP), spotting trends and exceptions, and even predicting future patterns. Regardless, what all these techniques have in common is that they turn neutral data into meaningful information.

The Goal of Analysis

As we have said, analysis turns data into information. In business intelligence, this means asking relevant questions of your data so that you draw the necessary knowledge to make business decisions and take actions that further the company’s strategy.

The Benefits of Analysis

Analysis is the step that lets users understand their data, turning it into information; without analysis, data loses its context and much of its meaning.
Analysis empowers users to ask questions of their data; this is the main way in which users are said to “interact” with the data. In this sense, the more the analysis interface allows users to obtain meaningful questions of their data, the more it is interactive.
Analysis lends the necessary answers that guide business end-users to making the correct decisions and taking appropriate action.
Analysis highlights the critical factors and points the end-user towards them. By doing so, it facilitates prioritization and makes the business process more efficient.

Analysis Best Practices

Leverage the power of the Web to make data analysis features easy and intuitive to navigate thanks to the familiarity of the Internet
Empower as many end-users as possible to analyze the relevant portions of your company’s data. Do so by choosing a Web-based solution that is licensed to be distributed to unlimited end-users without additional cost, as is the case with server-based licensing.
Use technology smartly. Technology and the features deriving from it are tools–ask yourself what goal the tools are meant to achieve, and design your analysis interface to attain those goals.
Set up your reporting and analysis solutions to point the end-users to the most critical items, using features like dashboards, key performance indicators (KPIs), automated business alerts, etc.
Make your analysis actionable, so that the cycle “see, understand and act” is rendered as efficient as possible.

The post Analysis is Turning Data into Actionable Information appeared first on insightsoftware.

Analysts

insightsoftware — Thu, 14 Jan 2016 20:03:40 +0000

Analysts are business users who need a mostly self-directed data analytics experience. On insightsoftware’s Continuum of Self-Service, data analysts are the power users. They want to bring in their own data, discover new insights and trends, and collaborate with others.

Analysts are inquisitive, technical people. They’re comfortable working with data and likely picked up the nickname “Excel Master” or “Big Data” at some point in their career. They’re measuring campaign effectiveness on a daily basis, and seeing what percentage of sales calls have been followed up on. They’re comparing revenue generated year over year to see what is working versus what isn’t. They’re focused on improving operational efficiency, and the metrics they’re interested in often change.

Specific examples of analysts include:

An accounts payable specialist who needs to generate revenue and collections reports for different teams to measure and collaborate upon for forecasting
A data engineer responsible for collecting and analyzing information across QC, shipping, and R&D departments to better optimize manufacturing processes
A finance analyst at the hospital who performs cost studies and allocations based on settlement claims and payment reconciliations so that they can provide recommendations for improvements in the future

The post Analysts appeared first on insightsoftware.

Analytics ROI

insightsoftware — Mon, 04 Jan 2016 22:07:01 +0000

Analytics ROI is the Return On Investment on embedded analytics. The components of the ROI formula are:

Timeframe – Quantitative analysis is performed over a specified timeframe for a technology investment, typically three to five years.
Benefits – The combination of the strategic benefits (e.g., revenue increase) and operational benefits (e.g., cost reduction).
Costs – The investment to develop and maintain the solution.
“-1” – The formula assures that a positive ROI is achieved only when benefits exceed the costs.

To calculate analytics ROI, use the following formula:

As an example, let’s say a commercial SaaS provider brings in $2 million in revenue per year. They expect that new embedded analytics functionality can drive a 10 percent increase in sales (to keep this simple, we’ll ignore annual compounding). Over three years, that comes out to $600,000 in added revenue. Because the self-service functionality is expected to free up half the time of a developer (and based on a $100,000 internal cost per year per developer), you also have a $50,000 per year increase in developer efficiency, so the total benefit is $750,000 over three years.

The costs are expected to be $50,000 per year in software plus $25,000 in expert technical services. If a developer dedicates one quarter of their time to this project, your developer costs are $25,000 per year. That makes the total cost $250,000 over three years. The formula looks like this: ($750k / $250k) = 3, so the ROI is 200 percent.

As a second example, consider an internal manufacturing application that helps process $2 million worth of product a year. Embedded analytics helps to streamline the process, reduce waste, and improve yield, all to the tune of 10 percent per year of total production. This results in $600,000 in savings over three years. And just like the first example, with $600,000 in revenue – if we make the same assumptions for additional benefits and for cost – we also end up with 200 percent ROI.

The Definitive Guide to Embedded Analytics

Download Now:

"*" indikerer påkrævede felter

Use Case*

Forretningsemail*

Fornavn*

Efternavn*

Job Title

Nuværende økonomisystem

Financial System Version

Product of Interest

What is your biggest challenge within your financial department?

What is your primary reason for attending?

Which solutions are you currently evaluating?

Country

State/Province

I'd like to see a demo of insightsoftware solutions

Having trouble?

Cookies are required to submit forms on this website. Aktiver cookies. How insightsoftware is using cookies.

Oplever der stadig et problem? Kontakt venligst vores websteds administrationsteam.

The post Analytics ROI appeared first on insightsoftware.