Category Archives: Business Intelligence

DIAD-Power BI-CloudMoyo

Know 5 key benefits of Microsoft Power BI before attending Power BI workshop

Power BI is a business analytics solution helping users to visualize data, create stunning dashboards and embed them in any application. It’s availability over a cloud platform means that it can be used without any capital expenditure or infrastructure support. It is free from legacy software constraints and is easy to start with. The most striking aspect is that it enables end users to create reports and dashboards by themselves, without any dependency on information technology (IT) team or database administrators.

What is Power BI Dashboard in a Day?

To increase awareness of Power BI and help adoption, Microsoft and its partners host multiple Power BI training workshops called ‘Dashboard in a day’. – This initiative offers the attendees (user of Power BI from beginner or intermediate level) the benefits and other potentials of Power BI. The event helps the users to know how Power BI can effectively deliver them their reporting needs. Dashboard in a day (DIAD) is an effort to make data analytics transparent for businesses. The users get a clear idea of where the data is coming from and how to have competitive advantages by leveraging Power BI in the business. Benefits of Power BI services, Power BI analytics, Power BI dashboards and visualizations, etc., are some of the major points of discussion that DIAD covers. The content of this one-day event also covers demonstrating how to implement Power BI, build basic Power BI visualizations and showcases various Power BI dashboard examples.

DIAD helps you understand that Power BI is competent to perform all the technical work and can simplify a tedious task to enhance the business!

Also read: 5 reasons why you need a Power BI implementation partner

CloudMoyo has successfully delivered numerous Power BI implementations, Power BI dashboards, self-service BI and end-to-end BI solutions to Fortune 1000 organizations. Apart from Power BI dashboards, we have expertise in all aspects of data warehousing, data modelling and the Microsoft Azure Data Platform including the design, implementation and delivery of Microsoft business intelligence solutions to customers. With our deep competency in delivering Power BI analytics and Microsoft business intelligence solutions, our experts have listed the features of Power BI that have captured the most attention in the DIAD sessions so far –

  1. Power to transform business: Users get a knowledge of how to format or shape any piece of information in a certain way and how to fix it at the source itself. Query Editor, one of the most potent & effective features of Power BI Desktop, allows many customary transformations like changing data types, transforming by adding a new column, splitting & merging, and adding a query. This feature helps bring the transformation in place i.e. resulting in effective formatting & visualization of reports.
  2. Power of Interactivity: Interactivity between the reports can be realized clearly once multiple visualizations have been added to it. To see visualization changing its output, a user can simply click a bar on a bar chart. Similarly, to view values of location charts, lists and KPIs just choose a location in map visuals. Turn-off the filter option in Power BI, if you don’t want to offer filtered-based options within the chart. Therefore, Power BI offers clarity and enhancement in structure, enabling you to put your report in action by shredding off time in creating & analyzing them.
  3. Advanced measure: Data Analysis Expression (or DAX) is a formula language used throughout Power BI. It works very similar to Excel, but it eliminates the complications of the piles of Excel reports. Therefore, with DAX, you can create your own metrics (like last quarter’s net sales) in Power BI easily and with a much faster approach. Power BI offers an advanced feature (Quick Measures) to create complex DAX expression like monthly growth, YTD, a percentage difference, etc.
  4. Extract hidden information: The ‘Insight’ option in Power BI allows you to check the hidden information on your data. Multiple charts are generated within the chart which have the potential to provide more effective and strong metrics. To revisit these useful insights, you can pin this visualization to your dashboard. This makes way for a next level transparency for data analysis in business. You can easily realize if more revenue is generated in a certain section/category for business. Therefore, this also helps in identifying trends and saving on costs.
  5. Excellent storage capacity: DIAD showcases millions of data sources in the form of multiple excel sheet or files. The spreadsheet/flat files with 11 million rows will not open or load easily in a regular machine. Even if you get success in opening the file in your machine, you will face issues in generating substantial reporting information from those data. However, Power BI has the bandwidth to load and transform millions of rows chart in a shorter span of time. Not only this, Power BI also has the capability to compress the file without compromising its quality and performance. For example, the total storage of these files is 420 MB, it can be reduced to 50 MB once it is uploaded to Power BI.

To emphasize the benefits of Power BI, CloudMoyo is offering qualified customers a customized, 10-day proof of concept to showcase the value of Power BI for your organization. We’ll demonstrate a simple, yet impactful, use-case of your choice, using your own data. We’ll use this to create a data model as well as a front-end report with visualizations, all intimately tailored for your business. Kick-start your Power BI journey here!

Artificial Intelligence-CloudMoyo

A complete guide on how to implement AI in your organization

Many C-level or IT decision-makers believe that the sheer volume of data sets the foundation of AI (Artificial Intelligence). Around 90% of the enterprises incorporate AI because it’s trendy. Many lack the required skillset and tools to use AI and mitigate complexities of the huge volume of data they have, unaware of the fact that AI can help them solve most of their business problems.

Why should you invest in AI?

Applications have evolved, and things have changed remarkably since the days of plain old reporting. Now-a-days, your applications can learn and understand where you could go, what you could do, who you could meet and even what you might like to eat. If you notice, all of this is predictive rather than reactive. This gives businesses a newer weapon to target their customers, improve processes and save costs. They can now understand customer behavior actively deliver personalized experiences rather than the traditional ‘one size fits all’ approach. In addition, applications can foresee relevant events ahead of time and aid decision makers to prepare for outcomes.

In short, AI strengthens customer experience, increases engagement, and builds strong targeted communication. It accelerates the decision-making process by helping in gaining competitive advantages. Instead of getting overwhelmed by the huge volume, variety and velocity of data, businesses can now use that data to realize the advantages of using artificial intelligence. Read on to know how to do it…

How to start with AI?

Ask these questions to yourself before gearing up for AI:

  • Are you done being overwhelmed by the mountains of business data and thinking of exploiting competitive advantages with it but don’t know how to do it?
  • Do you want to understand your customer better and increase the retention rate with innovative use of your business data?
  • Are you looking up for improving your customer behavior?
  • Want to explore more and identify many other/new sources of revenue?
  • So, step zero is to find and identify the key business problems and know your business priorities. Continue reading if any of the above-mentioned goals sound like you and that if you have enough business data to accomplish (any of) these goals.

Here is the complete guide to follow if you want to implement AI in your business:

  1. Collect and access appropriate data: Sounds basic? Well, it is one of the most important steps to implement advanced analytics. Simply begin with the place where your data lives.
  • Check the type of data that you’ve captured so far – structured or unstructured
  • Evaluate if there’s any governance in place
  • Identify how to find high quality data
  • Categorize each data (by adding metadata, tags etc.)
  • Start small. Don’t try to document each and everything. Just focus on collecting and accessing those data points that can make you solve your business priorities and issues.

Also read: 5 myths about your data quality that can derail an analytics project

  1. Formulate a hypothesis: You’ve successfully created a data inventory. Now, what’s next? –
  • Try to correlate your accumulated data with your business goals and challenges; Think how it will help to achieve your business objective
  • Organize the given data to manageable chunks
  • Map out your findings
  • Stick to your priorities and try to work with what you have got
  • Understand what data you’re allowed to stock up and use. Consider data ethics.
  1. Narrow things down: It’s time to focus on what matters to your business. Now, that you know what data is important and what will help you achieve your business goals, keep all your eyes on it—
  • Catalog it for future purpose
  • Don’t indulge yourself in analyzing everything at the initial stage itself; give it a time
  • Concentrate on the datasets that matter to you
  • Be 100% accurate to achieve success.
  1. Test your data: It’s high-time to create a prototype and test your accumulated datasets.
  • Ask as many questions you want to ask at this stage
  • Program the algorithms to find answer to the queries. Use relevant data
  • Look for the pattern and behavior
  • If you think you’re not capable enough, partner with someone who can bring fresh insights and experience
  • Demonstrate something tangible from your data-Its value and worth
  • Make the prototype speak
  • Document the usage and outcomes of the prototypes
  • Get more people involved like a data scientist, etc.

Also read: Unsure about prototyping a data project? Here are our tips to run a successful Proof of Concept

  1. Make it happen: It’s time to make your data speak in real-life business scenarios.
  • Integrate the prototype into their existing business process
  • Use your findings to enhance the existing process
  • Operationalize and standardize the data insights to share with the entire organization.
  1. Put your data to work: The final step is to make your data speak at real-time, real-life. Create value and readiness for AI in the long run. See if your data insights are now converting into valuable and actionable business insights.
  • Monitor the process and start from step One to sharpen your data
  • Identify other cases where you can apply data technology
  • Check if you’re all set to use various components of AI such as Bots, NLP, intelligent automation, predictive analytics
  • Know where to use your algorithms for better results
  • Take a human-centered approach to AI and add value to your organization.

Definitely, AI has limitless potential in transforming the way you do business. It will play a huge role in the growth and success of your business, but you may encounter some challenges while implementing AI. Check out some of those high-level pain points:

  • Lack of technical know-how
  • Noisy datasets
  • Expensive human resources
  • Weak computation speed

Nervous about applying artificial intelligence to your business as you think you’re not ready for this? Allow us to help you achieve this milestone. Take advantage of our 5 day data modernization assessment where we take you on a journey to explore how your data can yield marvelous results Contact us today.

Self-service BI-CloudMoyo

Understanding the concept of Self-Service BI in the cloud

Do you use self-service BI? No? Think again…

How often have you selected few parameters, set some filters and got a report of your banking transactions from the internet banking facility of your bank? Or as a web marketer, haven’t you configured custom reports to get insights into your website visitors and their behavior? These are just two examples of how we have used self-service BI without realizing it.  Similarly, enterprises can also benefit from self-service BI.

Also Read: 6 Business Intelligence challenges that every organisation face

A Sneak-peek into the concept of Self-Service Business Intelligence:

Before we start to understand about Self-service business intelligence (Self-service BI), let’s take a glance at its concept and check if your enterprise is ready for the implementation of this technology. Basically, Self-Service BI helps users by letting them generate their own customized data reports and analytical queries without IT intervention. Hence, with these kinds of tools in hand, obviously, a business user can make well-informed decisions based on factual data. This will eventually allow the business user to yield progressive business results.

The cloud is drastically changing the landscape of business intelligence (BI). Users get major advantages from newly developed cloud apps than traditional BI leaders. It offers similar capabilities at a fraction of cost.

Why should you go for Self-Service BI in the cloud?

Cloud BI possesses the ability to solve user’s problems, which they are currently facing in the on-premise BI solutions. Cloud-hosted BI is always a good idea to go with! It not only takes off the burden of managing and controlling fundamental infrastructure- from the IT team but also offers them an easy yet trustworthy ways to store data and back up them. It increases productivity by improving the uptime and making the server available 24*7.

Self-Service BI gives the user a freedom to satisfy their analytical needs with zilch reliance on IT team, leaving them to concentrate on bigger and complicated organizational problems. This also allows the business users to make business decisions with faster approach. These advantages are win-win for every business user as well as for the IT team of any organization. However, many businesses fail/struggle while deploying and implementing self-service BI in the cloud, let us find why and how?

How to make Self-Service BI implementation a success?

In order to make Self-Service BI work, you need to first develop data warehouses, the processes of Extracting, Transforming and Loading data (ETL), generate data parts, dimensions, cubes and all those mechanisms and elements that are comprehensive to any Business Intelligence solution. This may take a bit of your time and effort in arranging and bringing all these resources in place. On the other hand, if you have already accumulated these components just go ahead with the last step of implementing Self-Service BI solution. One wrong step, lack of resources, effort and time may ruin the implementation procedure of your complex and large BI project.

Regardless to say, your data warehouse consists of confidential data that cannot be left open to everyone. Also, system may get overloaded if an undefined amount of users gets access and freedom to use the BI system on a self-service basis. Consequently, the system will get bogged down and will go through severe performance issues like providing conflicting data, irrelevant reports, bandwidth shortage etc. Hence, this is the stage where you need to draw the limitations pertaining to which business users will get the access to the BI system on a self-service basis.

Don’t know how to choose self-service BI? Read this blog on Top 10 factors to consider while choosing a Self-Service BI solution

How Power BI Can Be Used For Self-Service Business Intelligence?

Let’s see how Power BI can help accelerate Self-Service BI and can enhance its implementation rate:

  1. It gives users the ability to generate report on the basis their data without having access to the original data model.
  2. It inspires users to know more about their data, also assures an easy maintenance of Power BI reports.
  3. Power BI feature of Quick Measures can help the users to perform powerful calculations easily and quickly. With this feature, users can do calculations with minimal effort and knowledge of Data Analysis Expressions (DAX). It has more than 200 functions and counting.
  4. With the preview version of Power BI, organizations can now easily deploy number of purpose-built dashboards and reports to a big group of business users allowing them to make well-informed business decisions.
  5. The capacity-based licensing model delivers scalability and enhance performance to the Power BI services and increases flexibility on how users can make use of the accessed data.
  6. Today, Microsoft Power BI has more than 70 data connectors which makes it stand head above shoulders above competition. So, for example, Microsoft Power BI includes a connector for MailChimp, Google Analytics as well as Salesforce. This is a powerful feature for non-technical users across functions to create their own reports.
  7. Last but not the least; Power BI is amongst the top data visualization tools. Apart from a rich library of stock visualization formats, there are plethora of free custom visualizations on the Office Store and others can be sourced from users using ‘Publish to web’ feature.
  8. With Power BI, you ask questions in ‘natural language’ and get the right charts and graphs as your answer.
  9. You can tell a data story with Power BI publish to web, and reach millions of users on any device, in any place.

Overall, the new features of Power BI seem to be very useful and helpful for pumping up the adoption rate of self-service BI across the organizations.

Sold out on the benefits of a self-service BI solution but hesitant to start? No worries, we can help you get started on this journey with Power BI dashboards using your own business data.

Difference Between Data Warehouse & Data Lake|CloudMoyo

Difference between a Data Warehouse and a Data Lake

Is a data lake going to replace the data warehousing system in near future? Whether to use a data warehouse or a data lake or both? These are some of the common queries raised by the business users. Businesses should understand the concept of both data lake and data warehouse, most importantly when and how to implement them.

A data Lake is a repository that stores mountains of raw data. It remains in its native format and transformed only when needed. It stores all types of data irrespective of the fact that whether they are structured, semi-structured or unstructured.

On the other hand, a data warehouse is a storage repository that stores data that are extracted, transformed and loaded into the files and folders. A data warehouse only stores structured data from one or more disparate sources that are processed later for the business users. Data extracted from a data warehouse helps the users to make business decisions.

Read and know-Towards which direction is the Data Warehouse is moving?

What is Right for Your Company- A Data Lake Or A Data Warehouse Or Both?

Organizations, nowadays, generate a huge amount of data and access the huge number of disparate datasets. It makes the gathering, storing and analyzing of data more complicated. Therefore, these are the factors to choose data management solutions- for data gathering and storing and later analyzing them for competitive advantages. Here’s where data lakes and data warehouses help the business users in their own way. Data Lakes can be used to store a massive amount of structured and unstructured data that comes with high agility -can be configured and reconfigured when needed. The data warehouse system as a central repository helps the business users to generate one source of truth. It needs IT help whenever you use the data warehouse to set up new queries or data reports. Some data, which is incapable of providing answers to any particular query/request, is removed in the development phase of a data warehouse for optimization.
Take a deep dive into the Microsoft Azure Data Lake and Data Analytics
Classifications give Clarifications

Let’s explore and classify a few points to present some key differences between the Data Lake and Data warehouse:

  1. Data: Data Lakes embrace and retain all types of data, regardless of whether they are texts, images, sensor data, relevant or irrelevant, structured or unstructured, etc… Unlike a data lake, data warehouses are quite picky and only store structured, processed data. When the data warehouse is in its development stage, decisions are made on the grounds of which business processes are important and which data sources are to be used. A data Lake allows business users to experiment with different types of data transformations and data model before a data warehouse gets equipped with the new schema.
  2. User: Data lakes are useful for those users who are looking for data to access the report and quickly analyzing it for developing actionable insights. It allows users like data scientists who do an in-depth analysis of data by mashing up different types of data, extracted from different sources- to generate new answers to the queries. A data warehouse, on the contrary, supports only a few business professionals who can use it as a source and then access the source system for data analysis. A Data warehouse is appropriate for predefined business needs.
  3. Storage: Cost is another key consideration when it comes to storage of data. Storing data in a data lake is comparatively cheaper than in a data warehouse. A data warehouse deals with data of high volume and variety, thus, is designed for a high cost storage.
  4. Agility: A data warehouse is highly structured, therefore, comes with low agility. The data lakes, on the other hand, requires to technically change the data structure from time to time as it lack a defined structure that help developers and data scientists to easily configure queries and data model when need arises.

Below is a handy table that summarizes the difference between a Data Warehouse & a Data Lake –

Basis of Differences Data Warehouse Data Lake
Types of data Stores data in the files & folders Stores raw data (Structured/Unstructured/Semi-Structured) in its native format.
Data Retention Do not retain data Retains all the data
Data Absorption Stores transaction system or quantitative metrics Stores data irrespective of volume and variety
User Non-cosmopolitan like the business professionals Cosmopolitan-the Data scientists
Processing Schema-on-write, meaning- cleansed data, structured Schema-on-Read, raw data which only transforms when needed
Agility Needs fixed configuration-less agile Configuration and reconfiguration are done when required-Highly agile
Reporting and Analysis Slow and expensive Low storage, economical

In the concluding lines, it is quite tempting to say, “go with your current requirements” but let me advocate you here that if you have an operative data warehouse just go for implementing a data lake for your enterprise. Alongside, your data warehouse, the data lake will operate using new data sources you may want to fill it up with. You can also use the data lake as an archive storage and like never before, let your business users access the stored data. Finally, when your data warehouse starts to age you can either continue it by using the hybrid approach or probably move it to your data lake.

Learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other BI tools.

Self-Service-Business-Intelligence-CloudMoyo

Top 10 factors to consider while choosing a Self-Service BI solution

According to the report released by Gartner, the global market of Business Intelligence (BI) and analytics software will expand to reach $18.3 billion by the end of 2017 and shall continue to grow till 2020 to reach $22.8 billion. 

Business users increasingly demand the access to important business data in real time. Business Intelligence (BI) is crucial for any enterprise as it draws insights from past records, foresees future events accordingly and helps avoid possible obstacles. Data visualization and analytics tools like Self-Service Business Intelligence helps achieve long-term goals. The craving for data in the enterprises has accelerated the Self-Service BI market, as it not only helps businesses to improve and grow but also to manage their operations diligently.

What is a Self-Service analytics?

It is a form of BI which encourages business professionals to generate reports without any IT assistance. Self-service BI, an advanced analytics tool, enables business users not only to have an easy access to the company data but also to investigate and manipulate it to spot any business opportunities. With this, you need not necessarily have to be technically sound. It is perfectly designed to structure status of metrics and to point out the relation between metrics and data points. This analysis is quintessential as it open doors for improvements and opportunities that will lead to refining business strategies.

Also read: 4 signs that your business needs Business Intelligence solution

Therefore, self-service analytics solution is a smart data preparation tool that gives access to multi-structured data and rises to data discovery in a business ecosystem.

Choosing a self-service analytics solution requires different approach from that of the traditional Business Intelligence solutions tool. To evaluate the potentials of a self-service tool, certain key elements should be taken into consideration:

  1. Faster Action in Discovery:  Involving or relying on reporting teams for data analytics process establishes delay in work. Users want to find solutions to their problems in real time. Self-service analytics solution should be enabled to discover answer faster than any other sources. A ready-to-use solution or an answer that can be used with slight modifications saves time, eliminates duplication and introduces awareness and accessibility of content as per the need.
  2. Access to Different Data Sources: The BI should be capable of providing the user different data sources that can be accessible to any user from anywhere and on any device. Supported data sources should also include contextual data rather than only traditional relational sources and data models. Also, the self-service becomes more resourceful when it provides metadata of each data sources.
  3. Data Mashing: The BI solution should provide its users necessary guidance for generating or acquiring data. A flashy and wizard interface is expected to provide not only the idea to acquire different data but also knowledge to discover relationship between them. The BI must support advanced data integration features as well.
  4. Easy Interaction with Data Reports: Different guidelines should be set for Casual users and for that of the Power users. While casual users can interact with the reports by using nominal filters and can opt to access guided analytics or emphasized data insights, Power users can be able to create, modify, manipulate and new business logic or calculation by using tool advanced features.
  5. Collaboration: The self-service BI should allow its users to share and reuse the data in different content format with external members. The tool should support call-to-action, allowing user to add comments or multiple elements and text analysis into a shared data or a single unit.
  6. Threat Control: The BI tool must include a feature to prevent security breach of the privacy compliance processes. The self-service BI should also support the feature that allows delegation of security administration to particular group of users with each department. It must also have an auditing trail to track the usage of the content.
  7. User Interface: Self-service BI should have a user-friendly interface that can adequately meet the requirements. All the features of the BI must support the corporate environment. Also, mobile environment should offer responsive touch interface in a native app.
  8. Data Governance Framework: The self-service tool should support data governance requirements of the enterprise in order to prevent proliferation of strategic or nonstrategic content.
  9. Monitoring and Data Insight: The efficiency of resources define versatility and performance of a self-service solution. Therefore, monitoring of a self-service BI is important to eliminate duplication and reduce overall storage spaces. This is why the solution must provide data insights like access of data, usage pattern, etc.
  10. Scalability: As the data volume and user of the self-service BI grows, the platform must scale to deliver consistent optimal performance. The scale-out option of a self-service BI must be taken into consideration by the large enterprises.

Aforementioned are the key consideration factors one must notice while choosing a self- service analytics solution. The proponents of Self-Service Business Intelligence and analytics believes that it tailors the rift created due to the lack of professional data scientists in place, hence it helps in making the required data available to the business user–the one who needs it the most! Now, data-driven decisions can be made in real time. To avoid mismanagement of data and effective implementation of self-service analytics in an organization, it is crucial to maintaining data governance.

Microsoft’s biggest entry into self-service business intelligence area is Power BI. It is a perfect blend of excellent analytics, smart & intuitive interface and perfect capabilities of data visualization. In 2017, it has added remarkably good features that will enhance the adoption of self-service BI. However, there has been no gap in new technical components for improving Power BI.

Self-service BI is best for your organization when it is modeled and deployed by experts, preferably a Microsoft Power BI Partner. If you have any questions or want to know more about how your organization can utilize Power BI for developing a self-service business intelligence solution, contact us!

To start free with Power BI, we are offering enterprises two free dashboards along with data architecture consulting completely at no cost! Sign Up Now

Power BI Implementation partner_CloudMoyo

5 reasons why you need a Power BI implementation partner

If you are still clouded on the idea of whether or whether not to have a Power BI implementation partner, you must read this blog.

The answer is yes!

Yes, you should have an implementation partner for Power BI. Power BI is a marvelous resource to any business. It is an asset that can bring maximum outcome with right configuration and from appropriate data pulling from all the sources.

Here’s where your implementation partner plays a pivotal role in getting your organization a significant value and competitive advantages with faster approach from business intelligence.

Relatively, Power BI implementation process is easy and simple than any other business intelligence. However, it is suggestible to look for someone who has a sheer knowledge of how to anticipate, mitigate and accept & react to data pulling challenges from disparate data sources and databases into your matchless and distinctive Power BI implementation. The partner should first and fore mostly have a pure understanding of implementation planning, execution and maintenance of Power BI

Here are 5 reasons why you need a Power BI implementation partner

  1. Domain Expertise: The Power BI implementation partner knows everything about the technology and its aspects. They have an in-depth domain knowledge as they understand what problems and issues may cause to a business if the software application is not implemented properly. An implementation partner, therefore, helps provide a uniform user experience. The Power BI implementation partner helps in seamless integration of client’s current business environment to their data sources and databases. This enables the users to adopt the abilities of analytics and reporting.
  2. Extract Value: Businesses have started adopting business intelligence as it helps in decision making process and hence bringing value to the organization. Thus, it is critically important to have a trusted implementation partner for Power BI. A Microsoft Power BI consultant/partner holds the ability to deliver high value to the organization they work with. Leveraging intuitive tools with in-depth technical expertise helps them effortlessly embed easy and interactive interface of the application.
  3. Trust factors: An implementation partner can be trusted to deliver, backed by its record of accomplishment and has established (trusted) relationship with other enterprises. In addition, more often than not, a system integrator boasts of various affiliations / alliances with industry bodies, technology players and an array of software products which enable it to provide quick support, fixes issues while ensuring that the work is not disrupted at any point of time.
  4. Core Resources: A system integrator can provide a variety of value-adds apart from plain vanilla product implementation. For instance, they can enable integration of multiple visualization tools to create robust, recyclable models over the data to deliver uniformity across reporting and analysis in your business. They also provide access to best practices gained through years of experience.
  5. Flexibility & Scalability : In addition to expertise, an implementation partner brings a vast array of resources which are available to a company on demand with option to scale up or down as per demand. This frees up core resources on client side for strategic tasks and also enables the client to focus on core operations leaving the technology aspect to those who can do it better.

Introducing Microsoft Power BI Partner

Being a Microsoft Power BI Partner, CloudMoyo provides assistance to companies who need a Power BI implementation partner with extensive years of experience. Our business intelligence consultants who possess Microsoft certification and expertise, exhibits strength in the Power BI market. What makes us a perfect choice of partner for any enterprise is our proven track-record of having an all-encompassing Power BI knowledge and our unprecedented level of understanding of planning and executing implementation program. What’s more is that as a Microsoft Partner having Gold Data Analytics competency, our expertise in Data Analytics and our commitment to provide a transformational business value to our customers by leveraging the Microsoft stack is globally proven.


How CloudMoyo could help you?

CloudMoyo, as a premier partner of Microsoft offers you the following:

  • Better implementation, post-installation and maintenance support;
  • Choose best package suitable for your business by understanding your current business environment;
  • Easy and effective implementation for quick Power BI deployment and to get robust return on investment

Want to get started with Power BI? CloudMoyo is offering a rapid 2 week Power BI PoC consultation where it tells you how to set up a robust enterprise data architecture. Grab this offer now!

6 Most Common Business Intelligence Problems

6 Business Intelligence challenges that every organisation face

In today’s technology world, data generated on a day to day basis from different sources is enormous. This data can have valuable information that can help executives to take effective decisions. Organizations use Business Intelligence (BI) to cater to this but only a proper utilization of business intelligence can help organizations to improve the productivity and ultimately increase the revenues. As per research, $1 invested in business data analysis may generate up to $10.66 ROI on average. But there is no assurance unless you are using your BI effectively.

#1: Lack of BI strategy

Organizations should proactively define the problems they trying to solve. Only then they will be able to identify the right Business Intelligence solution that will suit their requirements. This is because once BI is implemented, executives should know the pros and cons of the solution they are using and how the solution could add value to them. Hence devising a strategy before adopting a solution is very important as confusion may lead to the failure of the adoption. Attempting BI without the fundamental preconditions for success in place is likely to be frustrating, painful, costly, and destined to fail.

A good practice would be to go for assessment and review the existing business processes. This will help to gather critical requirements necessary for laying out a proper roadmap and devise overall Business Intelligence and Data Management strategy. This should be followed by a Proof of Concept (PoC) to validate the solution and create a business case.

#2: Business Intelligence when you don’t know how to code

Now a days, executives find it difficult to access the right data at right time. And even if they do find what they’re looking for, data formats are typically so complex and unstructured it’s hard to find out meaningful and relevant data. Now unless they are using Excel extensively, they probably would not get much satisfaction (or value) from their BI system.

A good practice would be to replace Excel Sheets with intuitive dashboards to make data more engaging, meaningful and eventually very powerful. Hence for this a BI Solution should provide the ability to create advanced filters and calculations all without coding. A self-service business intelligence solution enables executives to create customized reports in no time with little involvement of IT once the entire solution is implemented.

Read 3 ways self-service BI can help your organization

#3: Lack of training & execution

Many a times, companies might have well-articulated requirements, a sound BI strategy, and a good tool solution, but lack technical skills like designing, building, maintaining, and supporting BI applications.   This results in BI applications to run slowly, break frequently, deliver uncertain results and eventually leading to rising cost of using the BI solution. The causes of lack of execution often are multiple and varied, as are its remedies.

Organizations should more focus on helping to understand their resources why is a BI solution needed and the benefits of a BI Solution as well. Resources should be in line with the executives on the gains that they can get by the use of their newly adopted BI Technology. Organizations should spend wisely on providing ongoing training, so that users understand how to use the system.

#4: Lack of BI impact (Low utilization)

Management might always wonder why there is no change in business results attributable to BI and might feel that business value of BI investments not captured. This indicates that the organization is not utilizing the BI solution at par with global standards and best practices. This is because executives are unclear on how their company could benefit from BI. Management may not be able to use information in the system and even may not be aware that it even exist. As a result, they are not satisfied with what investments in BI have yielded the organization, and therefore are reluctant to approve any additional funding for BI. They might even pull the funding, and spend that budget somewhere else.

What should matter to executive is how they use data and how accessible the data is in order to do something with it. It’s time for business intelligence implementations to stop relying on dull, uninspired pivot tables and spreadsheets and start presenting data in compelling visuals that are easy to understand and loaded with insight.

In such case, Executives and the BI Solution they are using to stop relying on spread sheets and start using actual BI to present the data intuitively. This will enable BI to unlock the full value of the data it gathers and deliver the desired ROI.

Quick Tip: Reclaim hours in your day when you discover how easy it is to analyze, visualize, and share insights with Power BI. Get started with a quick assessment.

#5 Business Intelligence with unstructured data

Most of the times data is unstructured for BI to analyze. This lead to a problem when users need to perform simple BI Processes. Businesses may invest in big data analytics but cannot complete the tasks in time. They may result to people spending hours on cleaning and structuring the data first and then using the BI solution.

A BI solution which could be loaded with automatic ETL capabilities to process data sets that need to be restructured will be a real solution here. This will enable users to create a single source as well as a front-end with data visualization capabilities. Ideally, the back-end of the solution would be able to manipulate the data for it to be analyzed in the front-end. Hence, the front-end will then allow users to visualize data in dashboards, reports and graphs.

Quick Tip: Use Azure Data Lake Store to unlock maximum value from all of your unstructured, semi-structured, and structured data. To know how this can help your business, click here

#6 Installation and deployment

A painful BI solution installation and deployment would be difficult to maintain. Even an unplanned & rushed deployment would be unsuccessful so often. Doing this may leave users void with time to understand the system and develop the skills using the solution effectively.

Executives can take a step by step approach to implement a BI solution. They can make a list identifying business problems and rather than expecting to solve every business problem all at once, they can try to prioritize specific outcomes they want to achieve. They can solve the issues consecutively until they have incrementally solved all the problems on the list and then think of implementing a BI solution

Conclusion: Done right, BI can be very effective

This repetition of common BI problems might demotivate businesses to lose and question the value of business intelligence. Use of BI can be challenging at the beginning but the potential business benefits make it worth the investment.  Having said that there is always a solution to a problem, these problems do have a solution as well

These solutions could be:

  • Treatment of BI as a business process improvement initiative rather than an IT centric undertaking
  • Focus on supporting key business objectives with better information embedded in specific business processes
  • Use of a BI-specific development methodology, such as Decision Path’s BI Pathway method

Many organizations don’t possess the internal BI expertise necessary to recognize their BI challenges and leverage these solutions to prevent and overcome them, but with solid strategy and guidance, organizations can harness the power of BI for improved business results and demonstrable business value.

Meet CloudMoyo:

If any of these challenges sound all too familiar, it may be time to consider Cloudmoyo, which helps managers and executives transform the way they run their business. CloudMoyo is specialized in the areas of Cloud (Azure) computing, Big Data and Advanced Analytics including the use of Power BI for visualizations. We are a Microsoft Gold Cloud Competency Partner and a Cortana Analytics Technology Alliance Partner. Our key service areas include Cloud & Analytics Consulting, Architecture, Migration as well as custom application development. We bring together powerful business intelligence (BI) capabilities in SQL Server 2016, Azure Analysis Services and Power BI to transform your complex data into business insights and share across your organization

4 signs that your Business Needs Business Intelligence Solution

4 signs that your business needs Business Intelligence solution

In today’s electronically interconnected world, the amount of data generated by business operations can have an inundating effect on enterprise systems. In this age of connectivity, businesses are flooded by a mountain of data which floods enterprise systems. To tackle this deluge, a number of big data technologies have emerged and businesses have deployed a variety of business intelligence (BI) solutions to identify patterns and trends within the data. As CIOs or other executives in IT departments, you may be often flummoxed by the plethora of Business Intelligence solutions. Should you initiate BI by implementing a Hadoop framework, or do you go for a more cost-effective, cloud-based system? Invariably, your business needs business intelligence!

How can you know if your company’s BI needs an overhaul? We have identified 4 signs that make it easy to see when a Business Intelligence solution is needed. Of course, you don’t have to wait until you see them all at once, and at least one is enough to raise a question mark.

  1. Multiple apps & data sources but manual processing:

Does your organization have multiple business applications and data sources but the process of putting them together is still manual? Such unharmonized data landscapes are common once businesses start growing and often processing this data is tedious. Data assembled in this manner is unreliable. Vast amounts of data continuously flow from different sources, and it’s up to companies to decide how they will use this sea of information to their advantage. Most of the time it leads to inefficiency and poor decision making. By using a properly architected BI Solution, you eliminate inaccuracy, obtain precise information about your business, and make that everyone is on the same page.

Quick Tip: Azure SQL Data Warehouse, a cloud based data warehouse hosted on Microsoft Azure is capable of processing massive volumes of data and can provide your business the speed & scale that it needs to manage enterprise data. To know more, get a free Azure Assessment from CloudMoyo.

  1. Complicated Reporting:

Is Your idea of business intelligence a spreadsheet? Does your monthly Report preparation loom before you as a cumbersome task? Using spreadsheets for analysis doesn’t mean you have business intelligence. Spreadsheet-based BI is a highly manual process that is prone to errors, and often delivers outdated and inaccurate data. Studies show that up to 35 percent of information within a spreadsheet worked on by one or more employees can contain errors. Spreadsheet data containing time-sensitive information can also pose an accuracy problem, frequently needing to be manually updated. Modern BI solutions automatically create and deliver real-time reports accurately and efficiently, allowing decision makers to initiate a well-informed course of action.

Quick Tip: Reclaim hours in your day when you discover how easy it is to analyze, visualize, and share insights with Power BI. Get started with a quick assessment.

  1. Lack of in-depth & customized data analysis:

Are you unable to perform an in-depth analysis of your data? Do you have to depend on IT for every bit of customization? That means, analytics within your organization is practically nonexistent.

It is not uncommon for analysts to find that all or part of data sets are missing or historical data is not fully available. Without access to historical data, a business loses the opportunity to understand their true performance over time and the ability to predict future trends. Loss of data most commonly results in inaccurate or less reliable insights and with massive volumes coming from a lot of sources, handling big data can take a lot of work. In aggregate, this results in poorer decision making over time.  Business intelligence can help companies avoid this. BI allows you to import and save historical data, and analyze for a range of different metrics, to give you well rounded insights.

Quick Tip: Use Azure Analysis Services, an enterprise-grade data modeling tool which enables a BI professional to create a semantic model over the raw data in the cloud using a highly optimized in-memory engine to provide responses to user queries at the “speed of thought”.

  1. It’s difficult to find important information.

When it comes to finding strategic information that goes beyond the daily operational requirements, it is not found. Important reports on sales statistics, cost analysis, and regional market saturation have to be hunted down and pulled together from different locations throughout the system. Trying to find the answer to a business decision that needs to be made by data analysis is like pulling teeth, requiring you to make special requests of employees to aggregate the data.

Not only is this taking away productive time from them, but it is not accessible to you in a timely fashion. This jeopardizes effective execution of mission critical or time-sensitive decisions. BI solutions are designed to have the answers at your fingertips exactly when needed.

Quick Tip: Use Azure Data Lake Store to unlock maximum value from all of your unstructured, semi-structured, and structured data. To know how this can help your business, click here

Conclusion:

A business intelligence solution is an investment that cannot be ignored by organizations anymore. It is not a luxury, as was the case with decision support systems or MIS in the past. BI serves an imperative need if an organization is to lock heads with its peers and gain the upper hand. A good BI solution is also not as expensive to implement these days as it used to be just a few years ago. Open source technologies, software as a service (SaaS), and cloud based systems have made BI as affordable to the small and mid-sized organizations as to their larger counterparts

All things considered, is business intelligence something your organization needs now? CloudMoyo has delivered successful Business Intelligence & Analytics projects for its clients across multiple industries such as healthcare, transportation, pharma, retail. A lot of this success can be attributed to a thorough assessment of client landscape followed by a proof of concept on real live client data. Most of these clients were able to pursue their enterprise BI projects after a successful PoC. With its expertise in deploying cloud based analytical solutions, CloudMoyo is the right partner for you to engage for your Big Data proof of concept.

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3 ways Self-Service BI will help your organization

Today, multiple sources are generating loads of data than before, but many still struggle with how to turn this data into actionable business insights. In fact, business leaders report they only use 30 percent of the data that exists within their companies. Historically, users were able to analyse their data using tools such as Microsoft Excel, but with this ever-increasing stacks of data, they often have to resort to support from their company IT department to generate reports. However, with overloaded IT departments and other obstacles like budgets and security, companies are not always able to squeeze the most analytical value out of their data. Also, IT is unable to turnaround this at the desired time and with the desired efficiency. With lot of data being real-time and the need to generate complex reports on demand, IT teams are unable to meet the needs of business users who want fast access to BI. This renders an essential business process into a bottleneck.

What is Self-Service BI

In today’s world with a multitude of BI tools, executives can now create customised reports in no time with little involvement of IT once the entire solution is implemented. For many organizations, the promise of self-service BI is one of freedom: Users are able to satisfy their analytical requests with less reliance on IT, allowing the business to make decisions at a much faster pace. And IT is freed up to focus on more complicated requests. These benefits make self-service BI appear like a win-win for business users and IT departments. Still, many businesses struggle with implementing it, and some even fail.

Common pitfalls of Self-Service BI

  • Often organizations start a self-service BI project without developing a proper business case or a Proof of Concept (PoC). This can help organizations to answer questions like where to start, departments to be involved, functional areas to be addressed, and what will be the return on the required investment. All of these aspects should be involved in your big data business case
  • Failing to build a team comprised of business users, BI experts and IT is a common fallacy in self-service BI projects
  • Strong master data management and governance is often lacking which leads to IT anxieties. Lack of proper authorizations leads to people having access to much more data than necessary causing frequent failures & issues
  • Many companies assume the skills needed to use self-service BI tools can be picked up on the job without any formal training, and this is a mistake
  • Failing to recognize users as either casual or power users and lumping them into one bucket can often lead to problems with implementation
  • Even if IT formally introduces a self-service BI initiative, it’s highly likely business users are creating solutions the IT department isn’t aware of.

Ensure a Successful Self-Service BI Implementation

In addition to making better use of data, a common goal with a self-service BI implementation is to create reusable data repositories business teams can pull information from, but that’s easier said than done. Let’s take a look at how you can help ensure the success of your self-service BI initiative.

Here are three ways how self-service BI can benefit your organization

  • Tailor-made for your business – Biggest challenge of most BI tools are that they are designed for the technical audience with complex interface and over-designed functionality. It is paramount that self-serve BI solution is designed with a non-technical audience in mind to help even a novice create and analyze their reports with ease and accuracy. Creating a self-service BI culture that’s customized to your organization can help you use data as a competitive advantage.
  • Beautiful Dashboards with a Brain – The purpose of BI is to reach actionable conclusions and present responsive dashboards to decision makers–and a lot of this depends on smart and beautiful dashboards. A lot of tools provide great charts, colors and graphics but lack a basic business intelligence technology at the backend to handle multiple data sources and heavy volumes. With data coming in at ad-hoc intervals as well as real-time streaming, it is imperative that users can create their own visualizations with a few clicks and without IT having to intervene. What’s more is that it should be easy to configure, manipulate as required.
  • Agile & reliable– Once implemented, self-service BI systems should allow quick and easy maneuvering without dependency on the IT department. These services, often provided by a third party partner like CloudMoyo with expertise in the self-service BI space, can provide assistance when you need it, and deliver reports and analyses in a timely fashion — freeing up your power users and IT team.

CloudMoyo has delivered successful Business Intelligence & Analytics projects for its clients across multiple industries such as healthcare, transportation, pharma, retail.

Read our customer success story: Power BI Embedded analytics for auto dealer incentive program

A lot of this success can be attributed to a thorough assessment of client landscape followed by a proof of concept on real live client data. Most of these clients were able to pursue their self-service BI projects after a successful PoC. With its expertise in deploying cloud based analytical solutions, CloudMoyo is the right partner for you to engage for your Big Data proof of concept. Book your 5-day Azure Assessment workshop now!