If you said “too much time,” you’re right. According to a Deloitte report, finance teams spend 48% of their time creating and updating reports. And when they’re operating at such a tactical level, it can be hard for them to see the forest for the trees.

Without a modern approach to financial reporting, finance teams are so bogged down in the details that they simply don’t have the time to uncover insights in the data 一 insights that could be vital to your business.

So how do you help them? In this piece, we’ll highlight five things you need to strengthen your financial reporting and be strategic in the data decade and how you can get them.

Comparing BI software to reporting software: which tool is right for you?

In this report, we define and compare business intelligence software and reporting software to help you decide which one suits your business needs.

Both business intelligence software and reporting software help businesses analyze their performance based on data-driven insights. But despite overlapping features and functionality, the two tools belong to distinct software categories. This report will identify the similarities and differences between the two software tools to help you understand which one is better suited for your requirements.

What is business intelligence software?

Business intelligence (BI) software helps organizations make operational decisions by analyzing historical performance data and other data sources. A BI tool collects raw data from internal and external sources and analyzes it via queries to get actionable insight. It uses interactive dashboards and other forms of visualization to present the data.

BI tools allow performance management teams to use descriptive analytics and convert complex data into easily understandable visuals such as charts, graphs, infographics, and animations. BI reporting helps analyze data from various categories, including customers, finance, production, human resources, and contacts.

What is reporting software?

Reporting software allows real-time access to administrative and operational data to help build multidimensional reports, such as financial statements, operational reports, and progress reports, from diverse data sources. It assists in tracking the success of sales strategies, monitoring responsiveness to regulatory compliance concerns, and managing internal controls or security audits.

Reporting software also supports the creation of custom reports per a business’s reporting needs. It allows managers and business leaders to measure and track performance metrics from departments such as sales, finance, human resources, and marketing. The software integrates with apps such as customer relationship management (CRM), enterprise resource planning (ERP), and business intelligence to create data summaries, dashboards, and data visualizations.

What do they have in common?

There are a few similarities between a business intelligence tool and a reporting tool. Both help operational teams create ad hoc reports, visualize data, and forecast business performance for strategic planning. The data reports generated by business intelligence and reporting tools include various similar elements such as charts, tables, key performance indicators (KPIs), images, hyperlinks, and drilldowns.

Business intelligence and reporting software shared features:

• Ad hoc reporting: Create business reports, which meet information requirements, on an as-needed basis.
• Customizable dashboard: Alter the layout and content of dashboard graphs and charts to strategically track statistics and metrics.
• Data visualization: Graphically represent data using elements such as charts, graphs and maps.

Business intelligence features:

• Collaboration tools: Provide a channel for team members to share media files, communicate, and work together.
• Data connectors: Connect to big data sources. 
• Key performance indicators: Evaluate company performance based on business metrics. 
• Metadata management: Manage and track reference data of files (i.e., tags, title, comments, date of creation, file size). 
• Performance metrics: Use a set of indicators to track how well an organization, a division, or a particular project is performing. 
• Predictive analytics: Predict future data based on historical data sets. 
• Publishing/sharing: Share and publish business intelligence data reports with the organization. 
• Self-service data preparation: Access, combine, transform, and store data without the help of an IT department. 
• Strategic planning: Visualize a preferred outcome at a high level, define goals, and identify specific steps to achieve them.

Reporting software features:

• Data import/export: Import and export information and reports to and from the program.
• Drag and drop: Assemble applications and processes by dragging over and arranging pre-built components.
• Forecasting: Form predictions based on past and present data/trends.
• Search/filter: Search available resources to locate required information.

Which tool is right for you?

If your business needs to collect, track, publish, and share performance metrics, real-time updates, benchmarking, and visual data analytics, then a business intelligence platform is a better option. BI tools offer advanced insights for analyzing data, benchmarking, and analyzing business performance, thus facilitating data-driven decisions.

A reporting tool, on the other hand, is a good option for micro-reporting requirements, such as reports on how many candidates appeared for an interview or how much revenue your business generated last month. Reporting tools can handle standard volumes of data and can draw limited data streams to produce the final reports.

A BI platform is commonly used by teams that influence business decisions. Therefore, it might have a more targeted end-user base than reporting software, for which the audience is more general.

Once you decide which software to use, head to our business intelligence software and reporting software category pages where you can find a sortable list of products, software reviews from verified users, and comprehensive buyers guides.

If you wish to narrow down your search to only the most popular and highest-rated solutions, visit Capterra’s Shortlist reports for the top business intelligence software and reporting software tools—our reports are based on an analysis of thousands of user reviews.

Author: Barkha Bali

Source: Capterra

1. Learn from your competitor's tests, not just your own. We all talk about testing, but did you realize that you can double your efforts by gleaning ideas from competitors? If you see what works for them, you can test it for yourself. And if you see something that doesn’t work, you can deprioritize that test, and put more lucrative efforts first.
2. Identify key subject lines, phrases, creative, etc. Chances are, if it engages your competitors’ audience, it will probably engage yours, too. It’s worth sorting through creative examples to get ideas for what you can test next.
3. Quickly see what is new in marketing. It can be difficult to find the newest innovations, tools, or techniques that can drive your results and make your job easier. A competitive analysis tool can help you keep tabs on your competitors so you can identify when they are doing something that you can’t. Think about all the technologies we now use that were virtually unknown 10 years ago: real-time suggestion engines, dynamic image generation, and more. Just by asking, “how did they do that?”, you might uncover that your competitors are using a new tool or technique that you could implement to help drive your ROI too.
4. Prove you need a bigger budget. A competitive tool can help you see exactly how much effort your competitors are putting into their email channel. Based on those competitive insights, you can prove that you need a bigger budget to keep up.
5. Track benchmarks. It’s helpful to understand how you stack up against competitive benchmarks, such as read rates or share of voice. It can be even more helpful to know how those metrics change in different seasons and during different holidays. This can support your budget requests or even potentially help you restructure your program.

How tracking the right KPIs and using the right triage strategy lead to success

Let’s start with a hard truth: If you try to do everything, you won’t excel at anything. In a growing business, there’s no shortage of things that need attention, but you can’t do everything at once. Instead, you have to decide where to focus your resources to get the greatest impact. In a word, you must become a master of triage.

Triage means making the tough calls. It means cutting program budgets to free up resources to run down existing leads. It means postponing the development of new features to shore up core functionality — or it could mean running the risk of alienating your existing customer base so you can develop a potentially industry-shaking new feature. In triage, there are going to be losers. But there will also be winners, and that is how companies survive, thrive, and grow.

To start, decide on your triage philosophy. Are you playing offense or defense?

Key differences between Business Intelligence and Data Science

Cloud computing and other technological advances have made organizations focus more on the future rather than analyze the reports of past data. To gain a competitive business advantage, companies have started combining and transforming data, which forms part of the real data science.

At the same time, they are also carrying out Business Intelligence (BI) activities, such as creating charts, reports or graphs and using the data. Although there are great differences between the two sets of activities, they are equally important and complement each other well.

Cloud computing and other technological advances have made organizations focus more on the future rather than analyze the reports of past data. To gain a competitive business advantage, companies have started combining and transforming data, which forms part of the real data science.

At the same time, they are also carrying out Business Intelligence (BI) activities, such as creating charts, reports or graphs and using the data. Although there are great differences between the two sets of activities, they are equally important and complement each other well.

For executing the BI functions and data science activities, most companies have professionally dedicated BI analysts as well as data scientists. However, it is here that companies often confuse the two without realizing that these two roles require different expertise.

It is unfair to expect a BI analyst to be able to make accurate forecasts for the business. It could even spell disaster for any business. By studying the major differences between BI and real data science, you can choose the right candidate for the right tasks in your enterprise.

Area of Focus

On the one hand, traditional BI involves generating dashboards for historic data display according to a fixed set of key performance metrics, agreed upon by the business. Therefore, BI relies more on reports, current trends, and Key Performance Indicators (KPIs).

On the other hand, real data science focuses more on predicting what might eventually happen in the future. Data scientists are thus more focused on studying the patterns and various models and establishing correlations for business forecasts.

For example, corporate training companies may have to predict the growing need for new types of training based on the existing patterns and demands from corporate companies.

Data Analysis and Quality

BI requires concerned analysts to look at the data backwards, namely the historical data, and so their analysis is more retrospective. It demands the data to be absolutely accurate, since it is based on what actually occurred in the past.

For example, the quarterly results of a company are generated from actual data reported for business done over the last three months. There is no scope for error as the reporting is descriptive, without being judgmental.

With regard to data science, data scientists are required to make use of predictive and prescriptive analyses. They have to come up with reasonably accurate predictions about what must happen in the future, using probabilities and confidence levels.

This is not guesswork, as the company will execute the necessary steps or improvement measures based on the predictive analysis and future projections. It is clear that data science cannot be 100% accurate; however, it is required to be “good enough” for the business to take timely decisions and actions to deliver the requisite results.

An ideal example of data science is estimating the business revenue generation of your company for the next quarter.

Data Sources and Transformation

With BI, companies require advanced planning and preparations for using the right combination of data sources to achieve the data transformation. To get appropriate data insights about customers, business operations and products, data science is able to create data transformations on the fly, using data sources available on demand.

Need for Mitigation

BI analysts do not have to mitigate any uncertainty surrounding the historical data, since they are based on actual occurrences and accurate and do not involve any probabilities.

For real data science, there is a need to mitigate the uncertainty in the data. For this purpose, data scientists use various analytic and visualization techniques to identify any uncertainties in the data. They eventually use appropriate data transformation techniques to convert the data into a format that is workable and approximate, which helps to get the data into a format that can be easily combined with other data sources.

Process

As you cannot get the data transformation done instantly with BI, it is a slow manual process involving plenty of pre-planning and comparisons. It needs to be repeated monthly, quarterly or annually and it is thus not reusable.

Yet, the real data science process involves creating instant data transformations via predictive apps that trigger future predictions based on certain data combinations. This is clearly a fast process, involving a lot of experimentation.

Whether you need reports over the last five years or future business models, BI and real data science are necessary for any business. By knowing the difference, you can make better decisions that will lead to business success.

Author: Brigg Patten

Source: Smart Data Collective

Self-service BI: explanation, benefits, features, do's and don'ts

Self-service business intelligence, or BI, has been on the to-do list of many organizations for quite a while.

Marketed as a tool that allows users from non-technical backgrounds to get insights at the pace of business, self-service BI, however, is leaving many organizations disappointed when it comes to implementing it practically.

Failure stories abound, with companies never getting what self-service BI has originally promised. That is freedom from IT for line-of-business users to create powerful and accurate reports to drive business growth.

In this blog, you will find out what self-service BI exactly is, why organizations fail at it, and what steps your company should take to implement a successful self-service BI solution.

What is self-service BI

Self-service BI definition

Self-service BI is often defined as a form of BI that uses simple-to-use BI tools to allow non-tech-savvy business users (sales, finance, marketing, or HR) to directly access data and explore it on their own.

Self-service BI differs from traditional BI that is owned by the IT or BI department as a centralized function. In the traditional approach, it is these teams that are in charge of everything. They prepare the required data, store and secure it, build data models, create queries, and build visualizations for end-users after collecting their requirements.

The idea of self-service BI is closely related to data democratization that is focused on letting everyone in an organization access and consume data. The ultimate purpose is to generate more insights at the organization level and drive better business decisions.

Key benefits of self-service BI

Faster time to insight

Shifting control to end-users means skipping time-consuming stages of the traditional BI process. In self-service BI, end-users don’t have to wait for days or even weeks until their report finally goes live after getting through elicitation and approvals. They also don’t have to deal with the tedious change request management process when realizing that more visuals are necessary. This is because they can chop, tweak, and add data on the fly to uncover important trends, patterns, or anomalies.

Improved operational efficiency

By empowering business users with thorough domain knowledge to perform their own data analysis on an ad-hoc basis, self-service BI produces better-quality insights while freeing the IT or BI teams from handling routine tasks related to data. Instead, these teams can focus on harder problems like setting up data pipelines to get cleansed and transformed data to the right destination at the right time and maintaining important data governance processes.

Cost reduction

Apart from optimizing IT and BI capabilities for time and cost savings, many self-service BI adopters take a step further. They arm subject matter experts with knowledge and tools for performing advanced data analytics. In other words, they raise citizen data scientists who know how to generate ML-driven predictions critical to business. With data science talent coming at a hefty price tag, this kind of investment is probably one of the best a data-driven company can make.

Core features of self-service BI tools

To enable the powerful benefits of self-service BI mentioned above, self-service BI tools should have the following essential features:

• Data connectors that enable self-service BI tool integration with databases, CRM, ERP, marketing analytics, finance software, and other on-prem and cloud systems to serve analytics needs in the most efficient way.
• Vast reporting capabilities that range from book-quality canned reports with customizable settings to ad-hoc drill-downs while allowing users to schedule distribution or divide the results into subsets for different audiences.
• Intuitive drag-and-drop or click-based interface that allows users to select data fields and visuals and drag and drop them into report canvas for exploration and storytelling.
• Data visualization templates that simplify the process of creating dashboards based on user preferences and needs.

Many organizations take their self-service BI to the next level by enriching it with capabilities in data science and machine learning. Augmented analytics platforms enable users to discover more data, evaluate uncharacterized datasets, and create what-if scenarios. This way, business can react to its evolving needs as quickly as possible, achieving the utmost nimbleness.

Why organizations fail at self-service BI

1. Unrealistic expectations

An organization that just starts throwing data at novice users is facing a serious risk of poor-quality reports. It will be very lucky if these users with different qualifications wind up with non-misinterpreted data without first learning the basics of reporting.

For instance, a happy user creating their first report on total sales in a historic period might end up with average numbers instead of a SUM, knowing nothing about default aggregations for various measures. Or on the contrary, they can submit inflated numbers. There is also risk of data inconsistency that might affect weighted averages when they need to be displayed with different levels of granularity.

Further on, a non-power user might rest satisfied with just a casual analysis that has supported their initial beliefs. The confirmation or cherry-picking bias trap is not something an untrained user is necessarily aware of, especially when under pressure to explain a certain pattern.

2. Reporting chaos

Self-service BI doesn’t mean zero IT involvement. Letting users toy around with data with no governance from IT usually leads to reporting anarchy.

With no governance, there could be redundant reports from different users working in silos and delivering the same analysis or reports from different users analyzing the same metrics but using different filters and hence delivering conflicting results. Reports from different departments can rely on different naming conventions for quantity, value, or time or use the same terms but not necessarily the same definition. Multiple versions of the same database, errors in databases that are never fixed, the creation of objects used only once … The list is endless.

Governance is not something that a data-driven organization can boycott in the world of self-service. No matter how badly a company wants to free users for conducting their own analysis, IT still needs to be involved to maintain high data quality and consistency.

3. Lack of adoption

Truth is, not everyone likes to work hard. Most business users just want a simple dashboard that will give them the numbers. Valuable insights, however, often lie levels deeper that go beyond plain business performance analysis.

Another psychological factor that may hold back an efficient self-service BI is resistance to change. It is not uncommon for many organizations in the early stages of their self-service BI journey to see frustrated business users coming back to BI or IT to request a report as they did in the good old times. Older approaches are safer.

Unfriendly self-service BI environment setups also might be a problem. What may seem for IT or BI teams to be an easy-to-use tool for collecting and refining results can have an overwhelming and demotivating amount of features for a casual user without technical skills. Pivotal tables and spreadsheets might be dull, but users are quick to revert to them when getting stuck.

10 tips from ITRex on how to implement self-service BI successfully

Below is a list of essential takeaways from ITRex experience in building efficient self-service BI tools for both smaller business and large companies, including for the world’s leading retailer with 3 million business users:

1. Set your self-service BI strategy

You first need to define what you want to achieve with self-service BI, be it as simple as reducing delayed reports or providing data access organization-wide. Self-service can mean anything to different people, so you should be clear about your project. It’s also important to understand early the scale of implementation, the types of users, their technical proficiency, and your expectations of deliverables.

2. Keep all stakeholders on board throughout the project

You should wrap your head around what your stakeholders look for in data and their data-related success metrics. Interview them to collect their functionality, usability, user experience, and other inputs. Then continuously ask them for feedback as you iterate. Apart from making sure you build a relevant self-service BI tool, you will also give your stakeholders a sense of ownership and improve their engagement.

3. Involve the IT department

This is also essential. Your IT has all the information on your data environment, existing data sources, data governance controls in place, and data access management. They will help you choose or build a self-service BI solution that is easy to maintain, monitor, and manage in terms of user access and integration of new data sources.

4. Set up a robust governance

Self-service BI governance encompasses the following:

• Data governance policies and procedures to ensure your data is consistent, complete, integral, accurate, and up-to-date. Here you will need to develop a broader data management strategy and adopt leading practices in master and metadata management as part of it.
• Governance of business metrics to define them uniformly across your self-service BI environment and rule out any deviations.
• Governance of reports to set a procedure for their quality validation.
• Data security to define who gets access to what data in your self-service BI and establish data lineage

5. Select the right tool

There’s no one-size-fits-all strategy. Your users have different needs and skills your tool should precisely cater for. You will probably need to balance between flexibility and sophistication to allow your users to ask new questions while staying self-reliant. A custom self-service BI solution will make it easier to achieve.

6. Establish a single source of truth

A single source of truth is implemented as part of solution architecture to enable decision-making based on the same data. For this, companies build a data warehouse or another kind of central repository that provides a 360-degree view of all their data from multiple sources and makes data access, analysis, enrichment, and protection much simpler and more efficient. It’s worth the investment.

7. Educate users

Three types of training programs for end-users are a must: 1. data analysis and visualization, 2) the basics of joining data and building data models, and 3) continuous peer-to-peer training.

8. Build a community

It will help a lot if you either establish a center of excellence or have an expert community on Slack or Teams so that your end-users know where to go to fill in gaps in knowledge.

9. Consider embedding BI specialists in business units

They will help drive engagement by increasing access to data for users with no analytical background and providing oversight as needed for better-quality reporting.

10. Start small

Choose a limited environment for starting your self-service BI project and build from there using an agile approach. This way, you will fix problems early before scaling up.

Watch this two-minute video of a project from the ITRex portfolio to learn how self-service BI augmented with AI can drive efficiency gains for a large enterprise if done right.

Author: Terry Wilson

Source: Datafloq

The top 10 benefits of Business Intelligence reporting

Big data plays a crucial role in online data analysis, business information, and intelligent reporting. Companies must adjust to the ambiguity of data, and act accordingly. Spreadsheets no longer provide adequate solutions for a serious company looking to accurately analyze and utilize all the business information gathered.

That’s where business intelligence reporting comes into play and, indeed, is proving pivotal in empowering organizations to collect data effectively and transform insight into action.

So, what is BI reporting advancing in a business? It provides the possibility to create smart reports with the help of modern BI reporting tools, and develop a comprehensive intelligent reporting practice. As a result, BI can benefit the overall evolution as well as the profitability of a company, regardless of niche or industry.

To put the business-boosting benefits of BI into perspective, we’ll explore the benefits of business intelligence reports, core BI characteristics, and the fundamental functions companies can leverage to get ahead of the competition while remaining on the top of their game in today’s increasingly competitive digital market.

What is BI reporting?

Business intelligence reporting, or BI reporting, is the process of gathering data by utilizing different software and tools to extract relevant insights. Ultimately, it provides suggestions and observations about business trends, empowering decision-makers to act.

Online business intelligence and reporting are closely connected. If you gather data, you need to analyze and report on it, no matter which industry or sector you operate in.

Consequently, you can develop a more strategic approach to your business decisions and gather insights that would have otherwise remain overlooked. But let’s see in more detail what the benefits of these kinds of reporting practices are, and how businesses, whether small or enterprises, can develop profitable results.

Benefits of business intelligence and reporting

Users managing their own reports: the rise of ad hoc reporting

What is ad hoc reporting anyway?

Ad hoc reporting is any business report or data analysis curated and created by users, as and when they need it.

Ad hoc reporting in business intelligence is in complete contrast with the managed reports seen in the early days of business analytics, which relied on templates distributed by IT departments.

The BI world is evolving

Self-service analytics and actionable intelligence infused into internal workflows are empowering more workers every day, presenting them with the insights from analyzed data that they need where, when, and how they need it. 

That may be the greatest strength and the unexpected drawback of modern analytics — the metrics a business already understands tell them about what has been important in the past, not what may be important now or in the future. When using a business intelligence (BI) tool, it’s natural to gravitate toward what was previously important because of factors like upfront modeling requirements and the ease of looking at existing analytics compared to the effort required to create new metrics. 

How can organizations deal with rapidly evolving situations in a timely manner and use analytics to ask the most relevant questions and tackle bigger challenges that could drive their next evolution? The answer is ad hoc reporting. The right BI platform empowers users with ad hoc reporting tools and removes the overhead of incorporating data into truly new insights, allowing far more freedom in asking the critical questions, without the effort trap of relying on existing analysis.

Whichever version of ad hoc reporting your company needs (one-off questions or big, game-changing analyses), the right BI platform will help you do it better and get the results into the hands of the people who need them. 

Enhance infused analytics with ad hoc reporting

Flexibility is an essential part of driving evolution at any company. When choosing a BI provider, make sure it offers genuine ad hoc reporting, and not just parameterized insights that you can’t alter or customize.

For example, a marketing team might rely on a recurring bit of infused intelligence, produced on a regular basis, telling you how many leads were created in the past week.

This is useful but may open up more questions for a savvy marketing manager than it answers. They might want to know, for instance, where these leads actually came from, how many were converted into sales opportunities, or what the demographic makeup was. To determine that, the user needs to be able to run or adapt a new query, not rely on something decided in advance.

Additionally, the marketing leadership org may task a data expert to pull in data from a wide array of locations and apply machine learning and other advanced analytics techniques to it in order to uncover strategic insights. In this case, ad hoc analysis (meaning the ability to ask deeper questions and present the answers where, when, and how they make sense for whomever needs them) has the ability to answer big questions and evolve the business. The right BI and analytics platform will empower users to dig deeper and take action based on the results.

GitLab uses Python and R to go deep into data

Cloud data storage has become increasingly inexpensive. Every organization, new and old, has some kind of cloud data warehouse or other solution, storing massive amounts of information every day. Turning that information into intelligence and opportunity isn’t easy. 

The data team at DevOps life cycle tool GitLab was charged with unlocking deeper insights from its data stores to answer critical business questions. 

“What was frustrating with our previous solution was that it was a process to get reporting done,” said Taylor Murphy, Staff Data Engineer. “With the architecture we had set up, we knew what the tables looked like, we knew the SQL query we needed to write, so we wanted a tool that would allow us to write the SQL and visualize it.”

And for many organizations, SQL and some self-service tools might be enough to get what they want out of their data. But as data stores become bigger, companies rely on more data sources to draw insights from, and the questions they ask become more complex, organizations are reaching for more robust tools: 

“With Sisense, we had the additional ability to move into Python or R to do something more complex,” Taylor said. “That opened up so many possibilities that everyone on the team was super excited about.”

Sisense’s impact was twofold, allowing the data team to ask bigger questions and to share those results widely, demonstrating value so its efforts could truly drive change. According to Taylor: “Sisense has multiplied the effect of the data org beyond the data team.”

The future of ad hoc reporting: Faster results, deeper discoveries

The right analytics and BI platform will infuse actionable intelligence for daily tasks into workflows, empower nontechnical teams to answer new questions as they come up, and allow data teams to go deep to bring back groundbreaking insights.

When choosing a BI platform, it’s not “either/or,” it’s both: Empowering both nontechnical users and data teams to ask questions on their own, in their own ways, is important. The most robust platforms give users tools to take data analysis to the next level, ask questions that can truly evolve the business, and put that actionable intelligence into the right hands, with ease.

Author: Scott Castle

Source: Sisense

Why BI reporting is superior to traditional reporting

The pandemic has caused a major change in the way we do business. Some organizations had already begun the digitization journey before the pandemic hit, providing them with a head start or those that have managed to rapidly digitize has helped their business survive and enabled people to work remotely. Some of the requirements included using cloud technology to store and analyze large volumes of data which can be accessed by employees, partners and other stakeholders. People cannot afford to wait for weekly or monthly reports to make critical company decisions or need to see this information at home.  The ability to generate accurate, relevant and timely reports is critical if a company is to remain agile. In this post, we will discuss a few ways BI reporting is superior to traditional reporting practices.  

The ability to turn raw company data into actionable intelligence is at the core of today’s successful businesses.

Data is increasingly more important to everyone’s role. Its value is in helping people do their jobs better and BI reporting provides a complete picture of how your business is performing.

BI reporting offers one source of the truth

Companies often have data stored in multiple sources such as ERP, CRM and third party. Traditionally, data must be combined manually into a single source, typically a spreadsheet.  While spreadsheets have their uses, they are notoriously error-prone and not a good option for reporting. A mistake in a single cell will invalidate the entire report. Additionally, multiple managers will often share a spreadsheet. However, when multiple versions of the same document are created, it’s nearly impossible to guarantee that everyone is using the most current version.

On the other hand, BI reporting integrates company data from multiple sources, so users always have access to one source of the truth. By consolidating disparate data into one discrete repository, data cannot be accidentally deleted or altered. Also, data is displayed on a BI dashboard in real-time so everyone works from the most current information.  

BI reporting is on demand 

As many executives know, traditional reporting is slow, rigid, and becomes outdated quickly.  Long, and often frustrating, wait times for IT generated reports are all too familiar experiences. Yet, executives and managers must rely on weekly, monthly, and annual reports to make critical business decisions. This can lead to missed opportunities.

In contrast, BI reporting enables everyone to access data, conduct analysis, and create personalized reports without IT involvement. Self-service eliminates the wait time for IT reports. Instead, users can slice and dice the most current data whenever they need real-time, actionable insights. Also, standard reports can be generated on a designated schedule. For instance, reports can be set to generate on Monday mornings in anticipation of weekly staff meetings. If more information is needed during the meeting, a customized report can be created on the spot with just a few clicks.  

Finally, free of the continuous demand for reports, the IT department has more time to focus their attention on other important tasks such as maintaining security or managing data resources. And, the IT department can apply BI reporting to develop strategies to grow the business and increase profitability. 

BI reporting gives granular insight

Traditional reports are static, only providing a summary of information without much detail. This means you cannot investigate which underlying factors are driving what you are observing.  Furthermore, static reports only provide the information you request. Since you can’t probe information you don't know is there, you are only getting half the story. A partial picture can lead to a wealth of missed opportunities.

Conversely, BI reporting is dynamic allowing users to select a metric and drill down into the underlying data. In this way, users are empowered to ask questions of the data and follow their train of thought to discover the answers. For instance, overall sales figures may be on target. However, drilling down will display sales figures by region, product line, and type. This detailed analysis might reveal the one product is over-performing, and that this is masking the declining sales of another product. With this level of granular insight, the sales team can work to boost the sales of the underperforming product to increase sales revenue overall.

BI reporting offers data visualizations

BI reporting presents data in the form of visualizations to help clarity complex information. A graphical depiction of numbers makes the information easier to digest, retain, and recall. Visualizations might be simple bar charts, pie charts, and maps. Or they might be more complex models such as waterfalls, funnels, gauges, depending on your needs. In either case, your team will be able to see all factors that are affecting performance.  Visualization makes it easier to identify patterns, trends, and new opportunities. They offer the ability to see changes in customer behavior so your team can respond in ways that drive sales and enable you to stay ahead of the competition.

BI reporting for month-end statements

Finance team using a BI tool to report on month's close can review and analyze financial statements directly. A BI tool with financial statement capability makes month-end financial statements more accessible and allows more people to understand the impact of operational decisions on financial performance faster.

By adding financial statements to business intelligence software brings active analysis, data drill down and dashboarding to the finance and management team with fully controlled user-permission.

Financial statements are created in the same tradition as the accounting team recognizes, but the process is automated for each statement. The finance team can quickly build financial statements customized to users’ access, so branch managers can see information relevant to their branch, and management can see information across the whole business. The statements can also sit across one or many ERPs so leaders can view the individual company, branch, regional performance and even the consolidated performance when required.

Now that preparing the financial statements is faster and simpler, the finance team has time to carry out in-depth analysis of the numbers. By preparing financial statements within a data analytics environment, you can quickly compare statements from one traditional period or outside of these timeframes - say one week to the next.

Transitioning from traditional reporting to BI reporting will provide the ability to see the whole truth, make better decisions faster and uncover new business opportunities.

Source: Phocas Software