“We live in an era of big data.”  

In the past year with the growing amount of Big Data, datasets have become so complex that traditional BI solutions either fail in getting, dealing, preparing or just understanding the data.  

But data is everywhere and growing with every click. We need to find ways to handle it and leverage it for our benefit.   

Google, Netflix, Spotify, Facebook, and Amazon are leveraging user data and mixing it with their unique profile to create new products. Such products are more likely to be loved by users.  

Similarly, governments and hospitals, are utilizing augmented analytics to find efficient ways to administer services.  

If an organization wants to thrive in the information age, they need to uncover the insights hiding in data. Digging through this data is tedious and tough but the right tools can make this process efficient and simple.   

In this blog, we are going to help you identify the solution to your changing data needs and their relation with augmented analytics.  

What is Augmented Analytics?  

According to Gartner’s 2018 research, Augmented Analytics Is the Future of Data and Analytics, “Augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, forensic discovery. and sharing. It also automates data science and ML model development, management, and deployment.”   

Augmented analytics is artificial intelligence that will change everything about business intelligence and business analytics.   

Machine-Generated Business Intelligence  

Traditional BI  

Analytics and business intelligence have been around for quite some time now. In 1958, Hans Peter Luhn, IBM researcher, published a white paper “A Business Intelligence System.” in the paper, he posited that because “information is now being generated and utilized at an ever-increasing rate because of the accelerated pace and scope of human activities.”   

According to him, to process this we will require new technological tools as organizations will need all that information for making better organizational decisions.  

Traditional BI was the first iteration of general-usage tools which focused on connecting to single databases and generating reports.  

Analysis was unsophisticated and under a small class of dedicated analysts. But there was immense potential for improvement. This improvement came in the form of the next gen-analytics and self-servicing tools.  

Self-Service BI  

The drawbacks of traditional BI, including the need for skilled technical workers, lengthy insight times, and poor-quality data analysis, were addressed with self-service BI. Today, almost all the BI tool claims to be self-service.   

Today, Self-service BI solutions possess user-friendly graphical interfaces. These systems can handle millions and billions of data drawn from multiple sources: in-house databases and cloud storage.   

This system can be used by non-technical users and get them ready for analysis to speed up insights and eliminate bottlenecks. Modern BI solutions make data security, governance, and access control simpler. 

Different Data Approach  

Lastly, self-service BI systems have dashboard-building features. These features offer a wide array of infographics and easy color selection to make insights look good. This gives users across the board access to insights and eases to tag other users and add them to dashboards. This leaves no need to reinvent the wheel. However, these enhancements aren’t enough and there is an immense need to approach data differently.  

Business Intelligence Changes Over Time  

BI tools are continuously evolving from monitoring performance to being sophisticated Artificial Intelligence-driven analytics platforms.  

Thinking About Data and Analytics Differently  

Modern analytics and BI systems have a lot going for them, but there are still places for innovation. We need to re-think about data itself. Data preparation could be simplified and the business-led aspects of the industry need to be countered.   

While modern BI solutions can handle greater volumes of data, even with self-service systems, cleaning that data is still a manual process. This leaves room for human error before the analysis has begun!   

With Augmented analytics systems, AI components will improve this process. BI solutions are great at showing users the insights but there is a small problem, that’s all they show. If someone wants to know exactly what they’re looking for, then chances are they’re going to find it leaves little room for unexpected results that they might have not been thinking of. This is exactly the kind of information that can have a huge impact on the organization.   

The AI-assisted system can help humans to tackle this problem and get more out of their analyses. Easy-to-use analytics tools, made by AI elements can change organizations and allow users to make smarter decisions.  

Another huge reason is data itself. Human activities never stop making new data as data comes from countless human and machine sources. This poses a huge challenge for IT departments to marry these disparate data sources.

By relying on BI to make decisions, they will understand that they can’t rely on a small cluster of analysts to do all the crunching and database management.   

In a modern company, insights can positively impact workers, and the next big idea could come from anywhere. Traditional BI doesn’t work for the new world. So, what does the future of augmented analytics and BI look like? 

Looking Toward the Future  

Data is called the “new oil.”  

Why?  

Because companies see it as a powerful resource.   

And, if you want to leverage this resource, it’s not enough to have just a small team to plumb data for insights. Data access has to be democratized, especially with the stakeholders. This will enable them to access intelligent, self-service solutions to find answers to an important question that matter most to them, with game-changing analyses.   

Besides, data stored in off-site, proprietary, and third-party databases have to be ingested easily, securely, and with fewer resources for IT. This will quickly reveal insights and you can easily share them with others in the company.  

Democratization and Self-Service:   

With technology becoming more and more ingrained in organizations, the frontline workers have become are more tech-savvy than ever. But there just aren’t enough database experts to handle the tasks to govern and clean the data.   

Companies can leverage an AI-assisted self-service tool to derive insights on the scale of modern businesses where and when they need them with an easy-to-understand interface.  

Delivering Maximum Value:  

Adding data to analytics software is the most difficult of getting insights. In the Modern BI era, we are seeing solutions capable of handling disparate sources such as cloud storage, in-house databases, app APIs, CSVs, live data streams to perform complex analyses.   

But what if BI tools became smart enough to understand the connection between data sources? That’d be a game-changer.  

There has been a great demand for options for analyzing data and sharing results. The interface needs to have multiple ways to access it and make sense. Users also want a social way that provides simplicity to share their insights.   

Advanced modern BI systems allow the user to interact via chatbots and AI assistants like Ok Google and Alexa. AI assistances will even suggest people share findings with a fast process of disseminating findings to key members.  

What’s Next for BI: Augmented Analytics  

Augmented analytics and BI tools, in the future, will be quite different than the current tools. Augmented analytics, today, integrates AI into the BI process to help in data preparation and insight discovery. This will change with the subtle integration of artificial intelligence and natural language processing into augmented analytics.   

In the future of augmented analytics, data ingestion, understanding relationship in data, insight discovery, and interaction with the platform will become more streamlined.  

An intelligent augmented analytics BI system will start helping users when they begin interacting with it. Instead of manual data cleanup process, data ingestion will be radically simplified and AI components will do much of the heavy lifting.   

AI elements, within a few years, will surface these relationships via visualizations. This will allow users to drill down and look for more insights. Seamless processes from data ingestion to insight searching will be a huge time saver.  

Augmented analytics systems will also handle Big Data as companies already have data needs in billions. Despite the amount of data or its origin, these smart systems will handle them. They will also understand the differences between the datasets, how they interact, and how to query them for results. Instead of waiting for the users to perform analyses, these systems will be active in interweaving and analyzing the data.  

If a user is actively searching for new insights, AI systems will help them prepare. However, even at this stage, it will be different under the augmented analytics framework. AI in augmented analytics will be free of human biases and reveal crucial insights that humans never realized were important. They will do this by observing the connections in the ocean of data and suggesting relationships.  

Moreover, advanced machine learning algorithms aren’t biased, or bound by preconceived notions. They will deliver insights where they are found and will even become spatially aware of it.  

Once the insights are served, these smart systems will be able to walk users through the data and help them gain understanding. This is very opposite to them just giving the information. This change will extend beyond using the platform as many modern BI solutions now have mobile apps. Chatbot or Alexa integrations have allowed users to interact with the data in different ways and analytics will keep building on this trend.  

Augmented analytics and BI will become immersive as new insights and data will be accessible.

Conclusion   

Data is now Big Data. Countless users and devices are creating new digital records every second. This data is being processed and stored in complex ways. However, traditional technology is unable to handle the ever-mounting information. Hence, we need more powerful and robust AI-augmented analytic systems to make sense of this data.  

Every organization and government needs an augmented analytics platform to connect to these databases to find relationships within the data. These relationships are then converted into crucial insights with the help of human users and effortlessly share across the entire organization. 

However, in the coming year, they will also need ways to work with Big Data especially the one that goes beyond the usual analytics systems. This data can completely reimagine how users can relate to data. Augmented analytics will change how users experience analytics and change the world by serving up unimaginable insights. 

Author Bio:

The post Augmented Analytics: Future of Business Intelligence appeared first on Tech Research Online.

Business Intelligence (BI) tools are the talks of the town!   

With the increasing importance of data in the business world, business intelligence tools had taken a center stage. More so because, they can make the complex task of retrieving, analyzing, transforming, and reporting data simple and less mundane.     

Today, most companies use BI tools to empower their teams with the right data. And, though there are plenty of tools available in the market, Microsoft’s Power BI is the best interactive data visualization and analytics tool for business intelligence (BI).    

The best part about the Power BI is that it offers many advanced functions for data analytics and you don’t have to be an expert to leverage it.    

We are not going to tell you how to do all the things you possibly could with Power BI. But we have touched upon this subject in our previous blog, you can read more about it here.    

In fact, it is very useful, regardless of the knowledge of data analysis. In this blog, we bring to you the top 7 tips on how to make the most of Power BI reports. Let’s get on with it:   

Tip #1. Keep it Simple    

Power BI offers an increasing number of visualizations that are available in the Power BI gallery. With this larger gallery of visualizations, you might feel overwhelmed to use all of them at once.   

But there had been many instances where people try to use multiple features and they often fail. This is mostly because when you put too many things on a dashboard your users will not necessarily find all the little features and click on all the slicers.    

In fact, for many of them, understanding the dashboard can be quite overwhelming. Hence, when you are designing a dashboard for ‘ordinary people,’ make sure it’s simple, easier, and clearer. So, focus on simplicity!   

And, remember, don’t fear the old-school tables as they are still the best way to present raw data. Also, try to avoid pie charts and treemaps for this data as users cannot see the difference between pie fields.   

Let me give you a better idea, below is the report for the sales volume per region. Take a look at it and try to tell which area among red and orange is bigger.   

The report showing sales volume per region in the form of a pie chart which makes it hard to differentiate between sales in Europe (red) and sales in North America (orange)   

On the other hand, look at this report below:   

The report showing sales volume per region in bar graph format which provides simplicity when differentiating between sales in Europe (red) and sales in North America (orange).   

Tip #2. Context    

One of the coolest features that you’ll find on Power BI is the cross-filtering capability. The software allows you to first connect two different charts with data and put them next to each other.   

Now, when you’ll click on an element of one chart, the other one will be automatically filtered based on what you clicked.   

This feature is immensely helpful for data comparison, kind-of-visual drill-downs, and simple analysis. In fact, you can actually use three ways of filtering and connecting data. This will make your analysis experience much better and easier.   

Let’s consider the example of project management: You can see time reported by people (top bar in the below example) and the time reported each month (the bottom bar). Here, you can see the different behaviors the interactions provide.   

Besides, a lot of data elements might greatly influence the ease of use of the report, especially for not advanced users.   

Tip #3: Divide and Conquer   

Filters are the most basic concept of data visualization and yet you will be surprised by finding how many filtering possibilities there are in Power BI. Here, are some of the most obvious ones.   

Basic Report Filters Panel:   

Visual Level Filter filters data only at the selected visual level to have some background data only for filtering.   

Page-level filters are applied to all elements on the page   

Report level filters are applied to all pages to see the data in the same filter.    

You select a filter and then immediately move to the next page. The filter will stay selected which will help you to see data in the same context:   

Report filters panel – for those who are supposed to go through pages to see data in the same filtering context.    

Two In-canvas Filters:   

Slicers (in-canvas filters) – are available as single or multiple selection dropdowns.    

Cross-filtering – can be used instead of slicers to include additional information. For example, you can create a checkbox list from a vertical chart and use it just for filtering by just clicking on the bar to filter out everything else:   

Again, let’s consider the previous example: you have a multiple-page report with pages that gives you an overview of hours or details of time reported under particular tasks.   

So, when using in-canvas filters, you need to select the project on each page individually. However, when using a report level filter, it is still selected when you browse through different pages.    

Tip #4. Create Hierarchies    

If you want to show data analytics on various levels of granularity using the same visualizations, hierarchies are your best bet. For instance, a program manager might be interested in project progress and time reported per month, while a project manager could be interested in a weekly level, in a project management domain.   

In such a case, creating a separate report is not an option as you will then end up managing a large number of cases. The solution to this problem is to design a report in such a way that it can be used by both. Here, the hierarchies can come in really handy.   

You can use hierarchies in three ways:   

It can be based on the data source, basically present in the data model   

It can be based on date and time data, basically, present any time data as a Year/Quarter/Month/Day hierarchy.   

Or, it can be based on more than one dimension. It will not make them visible but allow them to drill from one another.   

Once done, the small arrows in the corner of the chart can go up and down the hierarchy levels. With the help of this, you will be able to achieve different perspective views from the same visualization and report.   

Tip #5. Focus on Quality, not Quantity   

Power BI offers so many features that some people could easily end up making a Picasso-like analytical painting. Just remember, you can add as many effects and colors as you want but that doesn’t mean it will hold much value.    

With Power BI, you will be able to produce any number of beautiful charts in a matter of seconds with any number of data pieces.   

Yet, it is more important than the time you spend on the tool should be spent on trying to visualize crucial information in that space. Your ultimate goal should be to present clear and easy-to-digest information to potential users at a first sight.   

Conclusion:  

The tips and tricks we have shared in this blog are the very basic concept that can help you create powerful reports. They should be simple and easily understood by regular users to create an impact.    

These tips can be easily incorporated by people across project management, finance, and development practices. They all focus on the users’ needs due to their simplicity and spend more effort on the most efficient way to tackle the particular piece of data. 

Author Bio:

Shreeya Chourasia is an experienced B2B marketing/tech content writer, who is diligently committed for growing your online presence. Her writing doesn’t merely direct the audience to take action, rather it explains how to take action for promising outcomes.

The post 5 Power Tips for Microsoft Power BI  appeared first on Tech Research Online.

Every single day, we are generating massive volumes of digital records. To handle this much information, we need more powerful and robust analytics and AI systems to store and make sense of it.   

The term augmented analytics is coined by Gartner. They say that it is the future of data analytics that harnesses disruptive technologies to automate insight discovery, data preparation, and intelligence sharing.    

Augmented analytics has the potential to merge traditional data analytics with technologies such as artificial intelligence (AI), machine learning (ML), and NLP.   

The next wave of BI tools and analytics will be different. They will change the user experience across the BI process with augmented analytics. Here’s how:   

• Data discovery, analysis, ingestion, predictions, and interactions between platforms will be streamlined    
• There will be easy share-ability and dissemination of results across integrated functions   
• Automate and democratize the whole data analytics/ BI process   
• More action-oriented experiences and cost reduction    

Augmented Analytics in Action    

Gartner says that augmented analytics marks another level of disruption in the analytics landscape.   

Data science, AI, and augmented analytics make analytics accessible for the organization. This, in turn, enables them to ask relevant questions and auto-generate insights in an easy manner.    

Augmented analytics systems recommend necessary metrics for your business which can then be analyzed. On the data preparation side, augmented analytics has the power to intelligently drive key insights automatically.    

For instance, assume a data point that indicates that revenue is down by 20%. You can dive deeper to uncover the true meaning behind it and why it is important.   

Augmented analytics will help you put perspective on the reasons behind the decrease. It may be either because your marketing isn’t effective, or is it because it is an industry-wide trend?   

It takes into consideration everything from analyzing the geographical spread, comparing relevant benchmarks, and giving a commentary around it.    

On the contrary, in today’s world, if you just knowing declining revenue you will lose time, money, and energy as it will not be valuable to your organization.   

Instead, you should focus on drawing out the reason for the decline. These are the only actionable insights. With this analytic method, you cannot only help deliver insights automatically but also flag certain threshold breaches.   

What are the Benefits of Augmented Analytics?   

Today, drawing out crucial and relevant insights from data is a huge challenge for businesses. Hence, it is so important for all businesses to invest in this new analytics method.   

It can make the search easier, speeds up a time to value, data literacy more accessible, and visualization faster across the organization.   

It is the best solution for large enterprises that are looking to reduce their analytics load on their teams, or from an e-commerce company detecting out-of-stock events to the order/relevance of news based on user behaviors, all in all, the use cases for analytics are broad.   

What are the Key Capabilities of Augmented Analytics? 

1. Data Preparation:   

Augmented Analytics solves the problem by reducing the process that data analysts need to automate repetitively every time they receive new data sets to work with.    

Also, it helps decrease the time required to clean data in the ETL process and allows for more time to find patterns and relationships, create visualizations, auto-generated code, and propose recommendations in the data.   

Lastly, it automates the process of data preparation, visualization, and analysis.   

2. Contextually-Aware Insights:    

Augmented Analytics takes into account behaviors and intents to create contextual insights. It presents new ways of looking at data and identifies patterns and insights based on questions that companies might have completely missed otherwise. It, thereby, enhance human intellect and transforming the use of analytics.    

It also highlights the relevant hidden insights that are extremely powerful capabilities. For instance, users can manage the selection state (context) at the step of the exploratory process.   

Besides, it also understands data values associated with or without the context. It results in relevant suggestions powerful context-aware.   

3. Enabling a Citizen Data Scientist:    

Augmented analytics can democratize data analytics and automated insight. It can do this by generation them through the use of ML and AI to convert insights into actionable steps. This can benefit companies by reducing their dependence on data scientists and making analytics accessible.   

Gartner says “augmented analytics is the future of data analytics because it moves us closer than ever to that vision of “democratized analytics” because it will be cheaper, easier, and better.”   

What is the Future of Augmented Analytics?

Augmented analytics is capable of communicating, analyzing, and visualizing data. Besides, it can also propose actions.    

Soon, this will have an inherently social component that will link analysis once insights are identified. It will then connect team members to those findings within the company.    

Going forward, we will see augmented analytics systems become more powerful and productive tools.

Author Bio:

Shreeya Chourasia is an experienced B2B marketing/tech content writer, who is diligently committed for growing your online presence. Her writing doesn’t merely direct the audience to take action, rather it explains how to take action for promising outcomes.

The post Augmented Analytics – Is This the Future of Business Intelligence   appeared first on Tech Research Online.

Power BI is usually considered to be a quick tool, which allows data display with varied visuals and graphics. Although Power BI has the capacity to manage huge databases, one may find himself waiting for a few minutes, till the visual load.

However, this is not a very big issue, and there are a number of good practices that can be used to off-load some burden form the machine that it has to carry out. And, most importantly there are various good practices that can be implemented to lighten the load and improve the performance of the tool while accelerating the process of loading visual and graphics.

In this blog post, we have listed down 5 quick and easy ways that are efficient enough to immediately fix the speed issues of your dashboard, while improving their performance without making any significant change.

Power BI Dashboard & Reporting

Power BI is a renowned business analytical tool that is used to create a dashboard and reports from your data. The ‘drag & drop’ interface of Power BI makes the process of creating a dashboard very easy and quick.

However, once you start adding tiles and widgets to the dashboards, the things start to get a little complicated, and the overall performance speed of your dashboard may get reduced. But this is not to worry about, because there are many ways that you can adopt to improve the speed of your dashboard.

Below listed are the 5 tips that you may use to improve the performance of your BI dashboard.

• Remove Everything Unnecessary & Unused

The first and the easiest way possible is to least burdened your dashboards. Whether its charts, tables, columns, rows, visuals, or any other thing, that is placed unnecessarily on the dashboard, or you don’t use it frequently, then remove it from your dashboard.

So when you are at the pre-processing stage of your data, try to stick to only what is really needed on your dashboard, because putting too much unnecessary stuff on the dashboard can significantly impact its performance and hence your overall productivity.

• Use Integers Wherever Possible

Another great tip to improve the performance of your Power BI dashboards is to use integers as much as possible. For your Power BI dashboards, searching through the rows of strings is way more complicated than searching through the rows of numbers.

So wherever possible, try to use integers as much as you can. For instance, instead of using ‘Yes/No’, try to use 0/1. Or, use 3/2/1 instead of high/medium/low. While choosing values, pay additional attention, because not every digit is an integer, so it always better to check the data format with the query editor.

Use Tabs

Try to use as many tabs as possible in your Power BI dashboards. It will not only decongest your dashboard but also improve its overall performance. So, if you ever find your dashboard overwhelmed by the charts, slicers, and cards, then it is wise to divide them into subtopics/themes, etc., and place them under different tabs.

By synchronizing your filters over different tabs, you will be able to keep your slicers, making tab B filter tab A or vice versa. This technique will make your dashboard looks organized, sorted, and faster in terms of performance.

• Stick to Built-In/Default Widgets of Power BI

Power BI is an excellent resource for finding out the remarkable widgets that accurately present your insights. Although sometimes, you are not offered any choice, even if you have one, we would suggest you stick to the default widgets.

Undoubtedly, those third-party widgets look way better than the default ones, but they seriously impact your dashboard’s performance. So before implementing, you need to ask yourself if they are worthy enough and if you can compromise the speed and performance of your dashboard for them. Not all commercial widgets impact the dashboard performance and speed; however, you will feel a significant improvement in speed if you will switch to the default slicers or widgets over the third-party one.

• Use Top-N Rows in Tables

Bigger tables can make your dashboard crawl. Whenever you use a slicer, the capability of your tables to generate and display the data into datasets comes into play. For a similar purpose, Microsoft has added the convenient feature of “Top-N Rows” features.

So whenever you activate the function, the tables limit themselves to the defined ‘n’ number of rows, instead of opening and displaying the whole dataset. This is another minimal yet essential intervention that can help you incredibly increase your dashboard’s performance.

Learn to Measure Your Speed

In the May 2019 Power BI update, Microsoft introduced the latest feature of ‘Performance Analyzer’.

This tool is designed to help you analyze your dashboard’s speed and also highlights the bottlenecks that are impacting the overall performance. It works by recording the actions that you perform while using a dashboard against the time that it takes to load a particular widget.

Also Read: Top 11 Best Big Data Tools And Software That You Can Use In 2022

Again, apart from these 5 ways, there are various other methods that you may use to improve the speed of your Power BI dashboard. However, before implementing any serious change, we would recommend you to try these to enhance the speed of your Power BI dashboard.

Author Bio

Sam Khan is a content writer and consultant in an IT company. He covers software development, mobile apps, Power BI & SharePoint services, and helps with the clients.

The post 5 Ways to Speed Up Your Power BI Dashboard appeared first on Tech Research Online.