23
May

Harness the power of analytics in manufacturing industry!

By coupling Information & Analytics, every manufacturing unit can adeptly oversee and streamline its diverse functions, including the shop floor, supply chain, sales, and post-sales service. This can be achieved by utilizing the available data to drive productivity and profit through data-driven decision-making programs.

Accelerate Data-driven Smart Operations with NRoot Labs Manufacturing Analytics

Our comprehensive self-service data analytics platform empowers organizations to harness operational data at every stage of the data lifecycle, from the shop floor to the top floor. This enables them to maximize value, minimize risk, and leverage the full potential of their data resources.

Improved Efficiency: Operational analytics helps identify inefficiencies and bottlenecks in manufacturing processes, enabling organizations to optimize resource allocation, reduce waste, and improve overall operational efficiency.

Enhanced Quality and Yield: By analyzing operational data, organizations can identify patterns and trends that impact product quality and yield. This allows for proactive measures to be taken, such as identifying and resolving quality issues in real-time, resulting in improved product quality and higher yield rates.

Faster Problem Resolution: Operational analytics enables timely identification and resolution of production issues by providing real-time insights into the manufacturing processes. This helps minimize downtime, improve equipment maintenance, and reduce the time required to resolve problems, thus enhancing overall productivity.

Optimal Inventory Management: Analytics helps organizations gain better visibility into inventory levels, demand patterns, and supply chain performance. This enables improved inventory management, reducing excess inventory and stockouts, optimizing storage space, and minimizing carrying costs.

Data-Driven Decision Making: Operational analytics provides organizations with actionable insights based on data analysis. This empowers decision-makers to make informed decisions regarding production planning, resource allocation, process improvements, and other critical areas, leading to better operational outcomes.

Predictive Maintenance: By analyzing operational data and equipment performance, organizations can implement predictive maintenance strategies. This helps detect equipment failures or maintenance needs in advance, minimizing unplanned downtime, optimizing maintenance schedules, and improving overall equipment reliability.

Just like how manufacturing industries convert raw materials into valuable finished products, discover how our solution on some of the key Manufacturing analytics drive our customers to achieving efficiency and value-added insights from their raw data! The below infographic helps explain the various analysis:

NRoot Labs is an end-to-end BI solutions provider transforming Data into Valuable Insights for Manufacturing Industry.

Are you ready to get started?
15
May

How can fill rate analysis help automotive businesses?

In any retail business a customer demand satisfaction is one of the most important factors to track. Was the needed item available in stock when your customer asked for it? To gain the trust of your retailers, you need to have an accurate fill rate.

Why fill rate?

The most important goal is to fulfil your consumers’ needs and provide them with an unforgettable experience! Good news is you will be able to achieve this with the help of fill rate. Determining this KPI is critical because if it remains low, it could lead to loss of sales, loss of customers, and a poor reputation. And with the accurate statistics at hand, a dealership will become aware of missed opportunities and take steps to manage them immediately.

 In an auto dealership, fill rate is the percentage of customer orders that a dealer can ship over a given point of time from the stock without placing backorders or missing a sale. In simple words, it’s an indication of how well you’re able to meet customer demand at any given time.

In order to improvise, auto retailers must ask themselves – “Why are fill rates so low, what is being done about it and what can we do to help ourselves through the challenges posed by low fill rates?” 

Let’s break it down!

To understand fill rate better, we must know the difference between service level and fill rate.

Fill rate simply answers one question – Was the client’s desired item in stock today? Whereas service level addresses issues such as how the situation was handled by the retailer. If the desired item was sold out and you were able to convince your customer to ship an alternative item or place a backorder instead. In this condition, your fill rate may take a hit, but your service level may improve as you are able to convince the customer to come back to you. There are chances that you may fulfil your customer’s request, yet their experience with the retailer may be unpleasant as they had to go for an alternative item.

In the broad sense, service level conveys customer satisfaction that can be a positive or a negative one. Whereas one can measure your dealership’s fill rate at any time throughout the year to determine how efficiently supply chain management meets order demands.

So, what is the ultimate fill rate and how do auto dealers improve it? The answer is – the higher the order fill rate, the better!

NRoot Labs is an end-to-end BI solutions provider transforming Data into Valuable Insights for Automotive Dealerships. To know more contact – sales@nrootlabs.com

References

16
Apr

Automotive Series: Top 6 Metrics To Track For Your Inventory Management

Why is inventory management crucial for an auto retailer?

Inventory can play a substantial role in the health of a business—having too much can cause problems, as can having too little. With an inventory management analytics, automotive retailers can get visibility into items across all channels, ensuring the right items are available, at the right time, in the right place.

Let’s discuss the key factors that you should be tracking –

Cost to market :

Effective dealers minimize costs to make inventory procurement efficient. Your dealership can make the most of its inventory by narrowing (not eliminating) costs and bringing in high-value inventory.

Inventory Turn Rate :

This is the overall in-stock inventory of your dealership versus your team’s monthly sales. The effectiveness of the dealership departments is key to increasing your dealership’s turn rate. Faster turn rates on fresh inventory will improve your GMROI across dealership.

Gross Margin Return On Investment (GMROI) :

The GMROI is a useful measure as it tells the average amount that the inventory returns after its costs. A ratio higher than one means the dealership is selling the stock for more than it costs to acquire it and shows that the business has a good balance between its sales, margin, and cost of inventory.

Average Days to Sell Inventory (DSI) :

This measures how long it takes for your dealership to turn its inventory into sales. When the measure is high, it may indicate that your inventory movement is inefficient.

Vehicle Demand versus Inventory :

Every vehicle has a window for maximizing its return on investment. Your dealership can determine the best-selling window by tracking a vehicle’s market value and demand. This information should also help you with your appraisal and inventory sourcing.

Mechanical And Cosmetic Reconditioning Time :

Auto dealers need to keep the inventory fresh and moving in an economical timeline. The longer a car spends in the shop, the lesser time it’s out for sale.  Efficient dealers solve reconditioning delays. 

NRoot Labs is an end-to-end BI solutions provider transforming Data into Valuable Insights for Automotive Dealerships. To know more contact – sales@nrootlabs.com

References

9
Apr

Automotive Series : Top 5 Metrics To Evaluate Your Vehicle Lead Management

If you can’t measure it, you can’t improve it.

As an auto dealer, you must create a digital sales funnel to evaluate and assess the success of your sales and marketing teams. Measuring the vehicle leads serves as a guide to new revenue opportunities to grow your business. It’s vital to know your performance indicators that explain which scheme attracted more inquiries or which campaign converted test drives to sales. 

Below, you’ll find five important vehicle lead management KPIs that will help you measure your business performance and boost your growth rate.

Let’s get started…

Number of Inquiries by source and contribution % :

helps understand which strategy worked best and by what percentage. This will in turn help direct the right resources towards the right scheme

Conversion % at every stage of funnel :

helps understand a prospect lifecycle and how many move to different stages down the funnel

Cost per Qualified Lead :

enables sales and marketing teams to set their sales goals, calculate potential ROI, and determine advertising budgets

Lost Sale Ratio :

explains missed sales opportunities that resulted by not fulfilling the demand of a customer request be it a specific product, feature, a discount amount, or even inconsistent customer experience

Trade in Ratio :

assesses the various strategies applied by used-car evaluators to attract customers to opt for trade-in schemes, especially when used car business is driven by supply

Over the long term, this will help the top sales and marketing team to analyze the data and deploy resources in a smart way to maximize lead management and better conversions!

NRoot Labs is an end-to-end BI solutions provider transforming Data into Valuable Insights for Automotive Dealerships. To know more contact – sales@nrootlabs.com

References

21
Mar

Automotive Series: Top 6 Metrics To Evaluate Your Vehicle Sales

Financial KPIs That Drive Successful Vehicle Sales Strategy

Tracking and measuring the performance indicators of your business is key to measure the successes and failures of your business and to make critical adjustments in your execution so you can achieve your strategic goals.

In this post, we’ll outline 6 most fundamental Financial KPIs that will help you keep close tabs on your vehicle sales performance and boost your long term success.

Difference between KPI and Metric

Facing an uncertain and disruptive future, how can automotive executives prepare? Which KPIs can chart the roadmap for the future?

Vehicle Average Selling Price(ASP)

The term average selling price refers to the price at which a certain group of goods or services are typically sold. Auto dealers can tweak this KPI to position themselves by finding a balance in pricing based on the market and their break-even point. This can also serve as a benchmark for entities who want to set a price for their product or service. It can be an effective way for sales executives to compare how various accessories, service contracts etc. are sold and their discounting strategy. Finally, it translates what the business thinks is the best and the most profitable route.

Gross Profit Margin Ratio

Gross profit margin is a profitability ratio that compares the gross margin of a company to its revenue. In an auto dealership it means how much profit a dealer makes after paying off their Cost of Sales (COS). Auto dealers need to competitively increase their GP ratio by marking up goods to sell at a higher rate. Another way is through Supplier Support to aid push inventory with suitable discount percentage to attract customers. This is a win-win model for both the OEMs and the dealerships as they make a sale and not have an aging inventory.

Net Profit Margin Ratio

A high net profit margin in an auto dealership means how effectively can they control their overhead costs and/or provide products or services at a price significantly higher than the cost. This can be achieved by efficient management by support teams, low operating costs and robust pricing strategies.

Average Gross Profit Ratio

Average Gross Profit is a profitability ratio used to calculate the percentage of profit company makes from its total revenue for a unit of vehicle sold. For an auto dealer it means how much gross profit they made per vehicle.

Fixed Overhead Absorption

Fixed absorption is the measure derived from operational costs that are covered for every unit of product sold.

Used-to-New Ratio

If an auto dealer achieves 1:1 used:new vehicle sale then they have done a “good” job, and if they have 1.25:1 (or more) ratio indicates a “great” job. This helps release pressure on the new-vehicle department for overall dealership profitability.

NRoot Labs is an end-to-end BI solutions provider transforming Data into Valuable Insights for Automotive Dealerships. To know more contact – sales@nrootlabs.com

References

27
Dec

Transforming Data into Value for a leading Automotive Dealer

Client


An authorized dealer of leading automobile brands such as Nissan, Infiniti, Renault, Great Wall and Dongfeng Motors, Ford and Lincoln. Being synonymous with quality automobiles in the GCC, the client has consistently set the standards for excellence, not only in sales, service and spare parts but also in customer service and satisfaction.

Background


The client was looking to support their growing business by transforming the IT operations and streamlining data management while improving accuracy. They were seeking a holistic view of their business to support their decision making.

Challenges


  • The IT landscape was not aligned to business value and outcomes.
  • The business users face the challenge of reporting out of Autoline Rev8 DMS which is not capable of supporting self-service and better visualization/dashboards to enhance decision making capability.
  • The users were dependent on tech support/DMS reports for extracting the data from the source systems to analyze & monitor KPIs outside the DMS system using excel & other means.
  • Decentralized data across multiple locations and multiple fragmented reports across functions made it hard to get the full value out of the data.


Solution


NRoot Labs was chosen as a trusted partner to implement and enhance information management by building a custom analytics solution for the dealership. NRoot Labs kicked off the project by putting together a digital transformation roadmap aligned to system readiness and business objectives.

The first step was redesigning existing reporting architecture for automobile business covering areas of Sales, Inventory, Service and Parts. NRoot Labs leveraged its unique data-extraction methodology from CDK. The data was then manipulated and shored up in the Enterprise Data Store in a usable format.

The team made data and analytics easier to access and use by deploying Microsoft Power BI to create interactive dashboards for several business areas. The deployment team also implemented data governance and change management guidelines for effective and secure use of data and analytics.

The client worked with NRoot Labs to ensure the data being reported was completely accurate.

Business Impact


  • The solution helped IT/business solve data management issues in ERP at faster time with greater standardization, transparency and 0% manual effort.
  • The report turnaround time is now reduced to 30 mins from 8 hours.
  • Department managers now plan strategies for low performing products at 10x faster than earlier as Power BI provides the information on the fly for various periods and departments at all product levels.
  • In turn, Stock availability based on the historic sales and inventory, each store P&L is increased by 30-40%.
  • The solution is being leveraged to improve After Sales efficiency and productivity.


  • Better planning of Menu Bundles/service packages.
  • Increase in upsell Revenue.
  • Customer retention in service is increased to 32%.
  • Decrease in customer churn and revenue leakage.
  • Better control of Obsolete category parts and better parts ordering process.



Create new value from data for your business now!

23
Oct

Self service BI vs. Traditional BI: A solution that IT loves and business trusts

As organizations shift their approach to analytics, IT leaders should seize the opportunity to redefine their role.

Adopting a collaborative approach to truly support self-service is the key to changing the perception of IT – from a producer to a strategic partner and enabler for the organization. Collaboration between the business and IT is critical to the success of the implementation. IT knows how to manage data and the business knows how to use the insights to drive business decisions. Early collaboration will not only lead to the deployment of a platform that meets the needs of the business but also drives adoption and impact of the platform overall. Indeed, in the 2015 Magic Quadrant for Business Intelligence and Analytics platforms, Gartner placed self-service data discovery tools designed for end users firmly in the forefront of the leaders’ quadrant.

Data Experts vs. Data Users

Owing to their complexity, traditional business intelligence (BI) tools have always been handled by data experts, meaning decision-making was limited to a privileged few. But not anymore. Today, In the world of BI, it’s all about the business user. Forget about those old tools that only data scientists or the IT department could use. In today’s fast-paced business environment, noone can afford to take weeks or months to prepare a report. It is all about being agile. Business users, at different levels, are able to access the real-time data they need & quickly generate results without the need for working on spreadsheets & any technical expertise. Often, no coding skills are required & even the reports can be sliced & diced as required without the use of Excel Pivots or any other methods of aggregation in SQL. The future is self-service, data discovery and quick insight—bringing power to the business users!

IT Governance vs. Empowering Users

As a self-service business intelligence solution, Self Service BI empowers the end user to seamlessly carry Self Service Data Analytics processes or Self-Service Reporting, without being heavily dependent on IT. When compared to Traditional BI, Self Service BI have a shorter development cycle that uses less IT resources and is quicker to deliver as well as easy to deploy on multiple platforms & devices. For example, reports can be accessed through smartphones & tablets. Because of the rich user interactive feature, user adoption is typically greater with Self Service BI than with Traditional BI as it is easier for any non-technical business user to understand & leverage. In Self Service BI Analytics, Data discovery is quick & easy, as it helps business to overcome the hurdles of Data Extraction & Data processing tools & pull data from different sources within their organisation (including the core data pipeline) to answer ad-hoc one-off questions. Once the Data-model is architected properly it helps information savvy users to get their answers fast without spending additional time on data-processing.

Self-Service Business Intelligence Governance provides ways for enforcing quick & affordable data governance. Business users are often unaware of the complexities of data preparation and the risks involved in getting it wrong. Without an authority guaranteeing strong data governance, they may miss mistakes in their own data, and draw the wrong conclusions—or different business users may draw upon poorly curated data sets, and reach different decisions. To prevent this, IT will most likely need to get involved.

Standardization vs. Customization

Each end user may be asking different questions, and looking for different answers. With flexible in-memory acceleration, users are able to create reports and answer questions for themselves, rather than having an IT department create reports for each question/user. Especially as data volumes grow, this becomes increasingly critical. With this in mind, self-service reports can provide a huge boost to productivity, as they can be personalised based on the individual requirements of staff. Self service reporting, enables an interactive reporting experience, as end users can make modifications and additions to their reports on the fly calling information to them instantly. Since report elements are chose individually, end users are able to ask their own questions of the data fully customizing their reports to match their data analysis needs. Additionally, end users should be able to interact with their finished reports by applying controls such as on-screen filters, sliders, conditional formatting, and by creating drill down and linked reports.

$$$ vs. $

One of the great benefits of Self-Service BI is the price, as well as further money that can be saved. With Self-Service BI being available over the Cloud, it can greatly reduce cost as no hardware need be installed. In addition to this saving, specific reporting staff are not needed as end users can report themselves, thereby saving money on personnel.

Standard Reports Vs. On Demand Reporting

Implementing ad hoc reporting functionality can be of major benefit to the entire enterprise. Self-service reporting puts the tools necessary to build a fully functional report and modify existing reports directly into the hands of end users so that data analysis can be achieved quickly, intuitively, and interactively with little to no training. Ad hoc reporting speeds the report creation process by empowering end users to work with their reports independent of developers. This helps to eliminate the lengthy back and forth cycle between end users and IT to achieve a final report, saving valuable time for both the end user and developer and allowing them to focus resources towards more mission critical activities. The platform gathers and stores all your cloud and on-premise info in a centralized location that you can access anytime, anywhere. You can also make use of pre-packaged content packs and built-in connectors to streamline import of your data from other sources (internet).

Dashboards and Reporting vs. Data Visualization as a service

An extensive library of visualizations and an intuitive drag-and-drop interface makes it easy for users to build data discovery dashboards. It will enable users to quickly visualize data with out-of-the-box grids, graphs, charts, and maps. If that’s not enough, it’s also easy to extend the visualization library to incorporate new third-party visualizations, like D3, or build your own from scratch with our Visualization Builder and SDK. Self-service tools allow them to visually slice and dice data, drill down to the bottom, and even change appearances with different chart types and a wide range of predefined templates for everyone. Visualization can change how business managers and other workers “see” data. The way data is presented to users will be as important as the information itself. But for achieving that, a flawless IT and BI architecture must be built that can deliver the right data to the right users and empower them to take advantage of it.

As an organization, you won’t get very far on self-service BI and data visualization if you don’t have high-quality data, a well-designed systems infrastructure, and consistent processes. A lot of work has to take place behind the scenes to build an IT and BI architecture that can deliver the right data to the right users and empower them to take advantage of it.

Making the transition to self-service analytics can be tricky and filled with challenges. Implementing few rules such as Embracing good data quality practices, Documenting Data definitions and lineage standards in data dictionary, embedding data science in reporting solutions, Developing security practices can will create a strong foundation for your team to build on. So, what’s next? Finding a trusted implementation partner can help. At NRoot Labs, our team of experts know BI and cloud computing. We helped many companies bridge that gap between IT and business users. Call us for a quick chat to discuss how you can bring the balance between IT and business at your organization.

28
Aug

Data is only as good as the questions you ask

Data is only as good as the questions you ask


Data Data everywhere, not a drop to act! Data volumes and types are growing at such a rapid rate. The immense resource makes it possible to be many things that previously could not be done and to do them significantly faster.

In many industries, the businesses are drowning in the wide expanse of data and are unable to use it effectively and to drive business value or insights.

Be Specific


No matter how many millions of dollars you spend on tools and technology – to capture, process and share information, the question remains – are you making better decisions?  Unless you ask the right questions and specific questions, your data will not provide the right  insights.

Asking Vague questions vs. Asking specific questions
How to raise my ROI? What are the channels we should focus on to raise revenue while not raising the cost?
What channels lead to bigger profit margin?
Which marketing campaign worked best?

 

What should I measure?


Getting down to basics: Key Performing Indicators (KPI)

It is key to ask the data analysis questions from the start. They lay the foundation to your BI framework.

How was sales last year? vs. What is the average Revenue Per Unit (ARPU)?
What is your Customer acquisition Cost (CAC)?
What is your Customer Lifetime Value (CLV)?

 

What are my data sources?


Ask your data team if the right data is available to address the specific questions. Identify the data sources you need now and for future and gather them in one place.

It is very important to make sure that your data is not skewed towards a subset, because it may not give you the bigger or the whole picture.

 

How to ensure Data Quality?

Your data analysis questions are designed to give a clear view of reality. If the data is incorrect, you may get  distorted view of the reality.

Read more about our Data Integration process

 

Who is the end user?

Knowing the end user will help you decide, what data and report you should focus on. Each type of end users, whether it is members of staff or someone from the management/governing body, has different needs and expectations. Some of the questions that you could focus on are as below:

  • Who are your end users?
  • How will they act on the results?
  • What do they expect from the reports?
  • What are their needs?
  • What are their technical skills?
  • How much time can they spend on he analysis?

 

How should I present the data?

Most valuable insights, if presented poorly can leave your target audience, without the impact you are hoping for. It is important to convince the end user that your data is accurate, reliable, important and urgent to act on.

Data in a spreadsheet vs. Drawing a picture with data
Data in a table Telling a specific coherent story

 

How to ensure a data driven culture?

With this information, you can outline questions that will help make key business decisions and set up a data driven infrastructure, teams and culture in a consistent basis.

Companies that have successfully cultivated a data-driven culture reap a multitude of benefits, from better employee understanding of the value of data and how to apply it to decision-making to a widespread commitment to backing up ideas with data and measuring outcomes across the board.

 

Without information, you’re simply another person with an opinion.

11
Jul

Bridging the gap: IT and Business Users

Gartner reports that CIOs’ top investment priority for 2014 – 2017 is BI and Analytics.

In a recent survey, 98% reported that they rely on business information to do their job well, but only 6% strongly agreed that they can access their business information quickly.

The right way to bridge the gap between IT and business users is by getting the right information to the right people at the right time. It sounds like we are over simplifying it, right? The good news is, we are in an age and time when it is getting easier to do this.

Self-service BI

Disparate systems, complicated UIs and a need for specialized tech skills have made business users more dependent on IT. The top management leaders need real-time access to critical metrics, and BI and analytics systems. To top this, the implementation time frames might be overwhelming too.

Self-service BI answers all the questions to this dilemma. It provides the facilities within the BI environment that enable BI users to become more self-reliant and less dependent on IT. These facilities focus on four main objectives: easier access to source data for reporting and analysis, easier and improved support for data analysis, faster deployment options such as cloud computing, and simpler, customizable, and collaborative end-user interfaces. It helps business users rely on real-time updates to occur automatically, thanks to APIs that plug data into a single dashboard.

Changing the role of IT leaders: CIO 2.0

The position of CIO has been changing and we are seeing more people in the role from less technical backgrounds than in the past. The main driver for this is cloud computing – a public cloud platform that hides more of the technical backend from the user than an in-house, physical platform.

The new age CIO will focus on what matters; driven by the business’s tactical and strategic needs. Rather than worrying what company label is on the servers, the CIO can look at the overall capabilities and performance of a cloud provider’s platform and services. He will ensure the services and functions they are providing the business meets strict criteria of performance, availability, security, compliance and so on.

This evolution of his role does not release him from the pursuit of the latest and greatest in technology. Although the separation from hardware to software means the general need to track speeds and feeds is less of an issue, what is happening at the software layer becomes more important. Being able to understand how disparate workflows between the company and its customers and suppliers can be integrated to provide the optimum business value is also essential, as is being able to ensure areas such as the internet of things (IoT) are dealt with successfully.

Every technology cloud has a silver lining

One of the benefits of cloud computing is increased efficiency. Services are rapidly deployed and ready for use in a matter of minutes versus weeks or months it traditionally takes. It makes your business more agile and tends to shorten IT projects. It takes fewer resources to deliver the project and a quicker and more predictive time-to-market. It has become much easier to start business innovation initiatives, often enabled by readily available cloud services.

Utilizing standardized services can significantly reduce issues and defects. This increases business continuity and reduces time spent on operational issues, focusing more on the things that matter. Cloud computing allows you to deploy the same service or topology of services repeatedly, with the same result every time.

So, what’s next? Finding a trusted implementation partner can help. At NRoot Labs, our team of experts know BI and cloud computing. We helped many companies bridge that gap between IT and business users. Call us for a quick chat to discuss how you can bring the balance between IT and business at your organization.

19
May

Benefits of Power BI as a self-service BI Solution

 

Whether you’re a fan of Microsoft or not, Microsoft Power BI is worth considering if you’re searching for a business intelligence solution.

Microsoft under the leadership of Satya Nadella has brought in fresh make over of Microsoft, and when it comes to Business Intelligence, the changes brought about under the leadership of James Phillips needs to be specially mentioned. Business intelligence (BI) delivers critical performance analytics and insights to workers, empowering them to make faster and better business decisions. However, enterprise-wide penetration of BI is still surprisingly low. This is partially due to the misperceptions that business intelligence is costly, difficult to use and deploy, and slow to deliver real business value. “Self-service BI” is shattering these perceptions. It delivers low-cost, rapidly deployed decision-support, enabling any worker, regardless of job role, geographic location or department, to work from a reliable and up-to-date set of data, presented in a context and detail level relevant to job role and appropriate to data access privileges. This definition serves as a useful discussion point to highlight how Power BI can help your organisation to achieve the objectives of self-service BI and realise its benefits.

Quick Deployment

A light semantic model over existing data: Power BI is tightly integrated with Microsoft’s other BI products and Excel, as well as having built-in connectors to external data sources and it empowers authorised users to rapidly set up new data connections and create new analysis reports. Power BI is a full-stack solution that includes data loading, data modelling, metrics, reporting and distribution. It can take the source data, and perform in-process data modelling relatively easily as well a providing an easy to use and powerful user interface for analytics and reporting.

Agile process to build new data models: It is the time it takes an organisation to create the required BI sematic layers that determines how agile or responsive it is. and with Power BI, you get a Full-Stack BI. It may come as a surprise to some, but Power BI can – with a little application – become a full-stack BI solution. This is because even though it is a single tool, it lets you carry out the key steps in a BI process. The table below lets you compare Power BI with the SQL Server BI stack:

Clearly, Power BI will let you do nearly everything that you can do using the traditional SQL Server BI toolkit. Power Query queries extend the scope of the data sources further to include Azure data services, OData feeds, web pages, web services, and SaaS applications. You can design, build, and deploy self-service BI solutions by creating Power BI reports from datasets, connecting to tabular models.

Power BI will ingest data from virtually any source

Connects directly to source data and Uses tabular models to integrate different data sources: Microsoft Power BI connects to most types of on-premise databases and they have a large and growing list of cloud-based connection options as well. Power BI has tightly integrated support for tabular models, Power Query queries and third party data sources. Collectively these provide access to all the data sources your users will need and covers relational, multidimensional, structured, or semi-structured data from on-premises, cloud, or web data sources. The easiest way to consolidate diverse data sources is to develop tabular models and Power Query queries. When data is stored in a tabular model then the underlying queries to the original source data are maintained so data refreshes can be scheduled easily.

Unlimited access to in-cloud and on-premise data: Power BI gathers and stores all your cloud and on-premise info in a centralized location that you can access anytime, anywhere. You can also make use of pre-packaged content packs and built-in connectors to streamline import of your data from apps such as:

Simple, Customizable and Collaborative Visualizations

Microsoft has built a brand new HTML 5 compliant visualisation engine that is simply awesome. Everything (virtually everything) will slice and dice and cross filter everything else. Anyone that can create a chart in Excel can create a chart in Power BI (and probably some people that can’t create a chart in Excel too for that matter). You don’t need a specialised IT report writer to create every new report for the business.

Making data more comprehensible: Power BI users can create custom visuals and use them in dashboards and reports. The number of visualizations is growing, however currently, visualizations include:

Sharing Data Insights: Analysts can utilize a free-form canvas for drag-and-drop data navigation as well as a vast library of interactive visualizations, easy report creation, and fast publishing. As Power BI is part of Microsoft’s set of business apps, it offers seamless integration with popular Microsoft software systems such as SQL Server, Azure, and Excel. PowerBI service BI takes this further and allows data insights uncovered by individuals, who have explored the data, to collaborate with colleagues-by sharing your personalised dashboards or by taking snapshots of individual reports and emailing them from within the Power BI apps.

Open source Visualizations: PowerBI allows you to share its visualisation source code so that any capable developer can develop their own visualisations and share them with the community. Developers can either copy an existing visual and extend it, or they can start from scratch and build something completely new.

Easy to Use

Minimal IT Support: Power BI is self-service BI at best. Microsoft has brought the rigour and structure from SQL Server Analysis Services into this product to ensure that it is a Self Service BI Tool with Enterprise strength features, including row level security, active directory integration etc while it is still familiar enough to Excel users that they can and will embrace it. Power BI tools can significantly reduce the learning curve for new users because report and dashboards have a simple and consistent interface that will already be familiar to many Excel users of Power View or Pivot Tables.

Augment data models with key performance indicators: A user can just drag data fields onto a visualisation within Power BI and it will intelligently interpret the data and decide how best to represent it. Power BI is made to interpret how fields are related, what type of data they contain, and whether they represent aggregations, calculations, unique values, geography fields, or hierarchies. The metadata is derived from the underlying data model can be enhanced with calculations, measures, KPIs, and perspectives (subsets of the source data), all defined using a sophisticated formula language called Data Analysis Expressions (DAX).

To streamline the experience even further, users can inquire the data and build visualisations from the results by using the natural language capability and just typing questions.

Power BI is Cheap!

Because users can get up to speed quickly and require little to no training, support costs with self-service BI are significantly lower than they are with more complex BI solutions. If you implement SaaS-based self-service BI, you can also recover direct server investments and evolving maintenance costs with a convenient monthly subscription. Also, self-service BI platforms scale seamlessly so that as adoption increases, additional server capability can be added without disrupting access or requiring significant IT resources. In marked contrast to traditional enterprise BI solutions, self-service BI tools don’t require either a data warehouse or the associated database licensing costs. This alone cuts down months if not years off of BI project timelines and it eliminates sizable expense. Power BI is free to try and indeed free to use as long as you want. If you want to take advantage of more of the enterprise features including controlled sharing of data, automatic refreshes using gateways etc. then you have to pay – wait for it – a whole US$9.99 per month per user. It is enough to give the competition a run for their money!

  • Power BI
  • Free – $0
  • 1 GB data capacity limit
  • Author content
  • Consume curated content packs for services like Dynamics, Salesforce, and Google Analytics
  • Create, view, and share personal dashboards and reports
  • Data exploration
  • Import data and reports from Excel, CSV and Power BI Desktop files
  • Native apps for iOS, Windows, and Android
  • Publish to web

Power BI is offered in three different plans, with a cost sheet showing the high-level features of each plan here, so you can decide which option works best for your organization.

The Power BI service is on a continuous release cycle. As improvements are made they are released into the cloud service. You can experience parallel improvements to the mobile apps and to Power BI Desktop. The pace of their release cycles s still incredible. Quite literally, today’s biggest complaint can disappear when you wake up tomorrow morning.

I highly endorse it and urge you to try for yourself and make your own evaluation. Have you used Power BI before? What was your observation?

NRoot Labs has tremendous experience and has the resources to architect and deploy comprehensive BI solutions with Microsoft’s technology stack on-premise or in the cloud. Contact us to learn how you can integrate Power BI into your overall BI strategy.

 

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