The Need for Analytics!
Data is undoubtedly the most valuable asset for any organization and with new inventions and innovations around the globe, it has become a necessity to ingest, rationalize, and express that data to make strategic business decisions. Need of the hour is accurate, prompt, pertinent, and reliable data, however in real time.
With instant gratification at your doorstep, real time data analytics is the new norm providing much required insights about every involved stakeholder ranging from the vendor to the consumer. Businesses around the globe are realizing the potential of harnessing real time data to get insights into daily business operations.
According to a recent survey, about 98% of organizations believe data analytics play an important role in their business operations and about 72% credit business improvement to analytics. However, the deployment reasons vary from case to case as illustrated in below figure:
Challenge in Modern Day Analytics
A successful business is one which can unleash raw data to realize business insights. However, despite the aforementioned benefits of modern-day data analytics, most of the companies are still novice when it comes to unleashing the full potential of modern data analytics. Most of the companies neither have the talent nor governance to use data.
Even though data storage and processing expense has reduced, complexities in data management has increased manifold. Even though these issues are handled through modern day technologies, the problem still persists – lack of synergy between the data strategies and business requirements!
Gartner says: “By 2020, half of the global enterprise world will actively measure and assess return on analytics platforms, so it will be to the company’s advantage to know precisely what it is measuring.”
Having knowledge of business goals is not suffice unless and until your employees are equipped enough to deal with modern day analytics challenges.
Gartner says: “Analytics can help drive business forward, but it’s up to the individual to use the available data to create new business opportunities. Putting data in the hands of all employees allows innovation to occur.”
Trust, accessibility, sharing, and data integration are the four important issues of concern before implementing any analytics platform. According to a recent survey affirming confidence of organizations in assessing these challenges, data integration is the most bothered issue with increase in data silos.
Figure below shows different confidence ratings of people implementing analytics in their organizations:
The workload of analytical capabilities is definitely going to increase in the future with enterprises agreeing on one ultimate truth – Analytics is paramount for businesses to have a competitive edge, and even survival!
Choosing the right analytical platform is the foremost priority for any organization and we are here to guide to for the same.
Considerations and Capabilities for your BI Tool
Implementing a business intelligence tool is now a necessity for organizations to achieve their goals whether long term of short term. The bigger picture lies in analyzing the impact an analytics platform would have on your business, is it going to drive any value or not?
Some organizations deploy an analytics platform to understand key performance metrics and operations, while some want to achieve data democratization to empower their workforce in making better business decisions.
Another important aspect to be considered is the decision on stakeholders. Before choosing your analytics platform, ask yourself the following questions:
- Who is going to implement and manage the platform?
- Will there be an internal or external board supervising the process?
- Is your target audience going to be the entire consumer spectrum, every naïve employer in your organization, or just a small segment of skilled analysts and IT experts?
- Do you want to deploy the platform for a specific business area or a broad spectrum of your organization?
Implementing a business intelligence tool is now a necessity for organizations to achieve their goals whether long term of short term. The bigger picture lies iDifferent BI tools differ with respect to many factors like self-service analytics capabilities, reporting capabilities, intelligence, data modelling, visualization, and many others. Here, we are going to list down important questions pertaining to the following factors as given below:
- Self Service Analytics Capabilities.
- Embedded Analytics Capabilities
- Storyboards and Storybooks
- Reporting Considerations
- Data Connectivity Capabilities
- Smart Analytics Capabilities
- Deployment and Administration Considerations
- Miscellaneous Capabilities and Considerations
Self Service Analytics Capabilities
As the name suggests, self-service analytics allows the user to be more independent in carrying out the BI tasks without depending on IT staff. However, self-service analytics goes beyond user independence and self-sufficiency. It actually depends on specific requirements of different users categorized as Casual BI users, Power users and/or Business analysts.
While a casual BI user would be content with analysis, dynamic reports, and storyboards, a power user would need BI tools to modify existing or create new reports and dashboards, a business analyst would require the BI solution for every part of their data driven decision making journey.
Before choosing your analytics platform for self service BI capabilities, ask yourself the following 15 questions:
Embedded Analytics Capabilities
Embedded analytics has the changed the dynamics of existing standalone analytical solutions by allowing organizations to combine analytics and operational applications into one system, saving the time spent in juggling between the two separate systems. Analytics can be now brought directly to the end users delivering contextual insights without any hindrances.
Research shows 71 percent of organizations attributed increase in revenue to embedded analytics while 62 percent reported being on a competitive edge over their peers in the market.
Even though there are innumerous benefits associated with implementing embedded analytics, still take a step back and answer the following questions:
Storyboards and Storybooks
Storyboards or Dashboards play the most important front end role in your analytics solutions. Businesses these days require much more than just static reports, rather dynamic dashboards continuously adhering to the ever changing data scenario. The requirement is not just to view existing metrics and statistics, but to explore data, discover hidden patterns, relations, and extract insights which would reshape the entire business process.
With multiple self service BI tools available around, designing a dashboard is not just meant for skilled IT staff, but also for the most non skilled users in an organization. In fact, advanced technologies like augmented analytics, machine learning, streaming anomaly detection are personalizing data storytelling, changing data driven business scenario, and benefitting the entire spectrum of business users, managers, executives, and customers.
A complete business intelligence tool always has a gaining edge over a simple reporting tool in providing overall outlook on data in form of valuable insights. Basic business necessities like tracking important performance parameters, monitoring operational data, creating and distributing reports need to be done in a smooth and intuitive way and this is where an effective analytics platform comes into picture.
Data Connectivity Capabilities
Key to a successful analytics solution lies in the ease of data accessibility. With a myriad of data sources around the corner, connecting to each is not the challenge, rather the perks of using wrong data which in turn might lead to disastrous business decisions. An effective business intelligence solution would be the one which allows you to establish a dedicated data environment meant for analysis without causing an overload on production data.
Smart Analytics Capabilities
Advent of new age technologies like artificial intelligence, machine learning, natural language generation has compelled conventional analytics to take a backstage while businesses these days are getting drawn towards the lucrative advantages of implementing smart analytics.
Organizations prefer platforms which provide every kind of analytical support starting from simple descriptive analysis stating the happening events to prescriptive analysis suggesting actions in response to those events.
Deployment and Administration Considerations
Emergence of cloud computing technology has resulted in a positive impact on businesses by economizing on irrelevant expenses while saving time and improving agility and scalability. However, it does comes with its share of cons in form of high costs and connectivity issues. An alternative is on-premise deployment with reasonable price and readily available IT expertise. Still, high capital expenses, maintenance woes, and installation issues make businesses prefer the other route.
So before choosing deployment option for your analytics platform, it is always advisable to do a bit of homework and with some due diligence.
Comparing Top BI Vendors in the Market
Enterprises are inundated with tons of data coming from every aspect including customers, vendors, products, operations, business partners, and many more. BI tools have moved a long way from IT supported traditional pixel perfect reporting engine to Self service support augmented analytics platform.
As mentioned above, there are many factors to be considered before concluding upon the best full stack analytics platform for your enterprise. However, the main trends which are most likely to influence buyer behavior include:
- Augmented analytics to lessen the burden of complex business data by automating many aspects of data analytics using machine learning and AI. However, the suggestion is to verify vendor claims before making the selection.
- Embedded analytics to integrate advanced analytics capabilities into existing business applications or portals, thus providing contextual analytics. The suggestion is to make sure the solution is flexible and usable for the most naïve non-technical users.
- Data Democratization to enable data access from every kind of source around the globe to each and every business user, eliminating data siloes. The focus is not just on static big data, but also real-time streaming data.
According to Market Watch, global BI market is most likely to reach USD 150 billion by 2025, at a CAGR of approximately 30%.
A review was conducted by Trust Radius on top 6 full stack BI vendors in the market and below is the result in terms of audience ratings out of 10.
|Self Service Analytics||Reporting||Embedded Analytics||Smart Analytics||Data Connectivity||Deployment and Administration||Usability and Performance|
However, before you jump into conclusions and choose to select any of the above-mentioned tools, take a small pause and look at some of the missing features for each product:
- JasperSoft does not provides predictive modelling nor supports ML models, predictive analysis, integration with R, nor pattern recognition and data models. Also, it does not support cloud deployment.
- Birst does not support single sign-on and embedded analytics. It also cannot schedule reports to remote servers. Also, there is a data modelling issue with insufficient ETL tools and occasional delay in applying filters.
- Sisense does not supports sharing and collaboration of reports, nor supports single sign-on, and embedded analytics. Also, the data mapping is quite complex with poor graphing and alerting features.
- Dundas BI might be a better choice, but it does not support Java API for embedded analytics. Also, there are redundant and complex visualizations, lack of filtering options (especially the calendar filter), and complex GUI layout with most of the tabs hidden.
- QlikView does not support single sign, embedded system nor provides predictive modelling. The other drawbacks include high cost, poor customer support, complex UI, and weak integration. Also, it lacks continuous intelligence and augmented analytics capabilities.
- Klipfolio does not supports many self-eservice analytics features like report formatting, customization, drill down analysis, etc. Also, it does not support smart analytics and does not data warehouse creation capabilities, ETL capabilities, nor OLAP support. Its other drawbacks include poor sharing, lack of time series analysis, etc.
Why BIRD is the promising Newbie?
Selecting the best analytics solution ought to be a well sketched and vetted plan aimed to procure value for the organization. The tools must be conscious of even the smallest change in business sphere and evoke instant response.
Even though BIRD is a novice product compared to its peers in the market, still we are quite optimistic about our proficiency in outshining other products in any field be it self-service analytics, embedded analytics, smart analytics, or data connectivity.
Instead of blindly believing, you can actually look at these facts below to substantiate our claims:
- Predictive maintenance on plant activities in Fortune 100 companies with real time IOT data monitoring and integration.
- Advanced ML models on warranty analytics providing failure forecasting, fraud detection, supplier segmentation, claim processing automation, etc.
- Vehicle part failure distribution data spanning 2 years, with approx. 5 million records, in a single heatmap visualization.
- Fastest and robust BI platform working on 1 billion rows and:
- Accurate ML models in the following fields:
At BIRD, we thrive to help you in achieving data democratization for your organization, where every single stakeholder can have access to data and leverage their analytics capabilities to improve decision making and solve a majority of business issues.
Our full stack data management platform enables you to transform raw data into active intelligence, thus lubricating the journey from data to actions. Our range of solutions include embedded analytics, real time insights, cloud BI analytics, conversational AI, and out of the box ML models supported predictive models.
Still a newbie, and we are already trusted by some of world’s largest brands:
And many more!…….