AAYS Analytics

Get Fully Customized Insightful Dashboard

Get Started with Aays Analytics.Let us get you actionable Insights and get ‘10x’ ROI on your BI journey

Get daily refresh to stay on top of KPIs with our highly scalable and affordable platform

Schedule Setup Product Tour

We are trusted by 50+ customer in 3+ continents

Frequently Asked Questions

What does BI stand for? How is it different from Analytics?

‘Business Intelligence’ (BI) and ‘Analytics’ are used interchangeably to generally refer to the practice of using data to make better business decisions. In general, ‘Analytics’ is a larger umbrella term and BI is a subset particularly referring to a set of technologies that support decision making within Organization in the form of reports, dashboards and interactive visualizations.

What comprises a BI solution? Dashboards are easily developed. Do I still need BI?

While the main output of a BI solution is always focused on getting reports, dashboards, interactive visualizations etc., there are various layers to provide a BI solution.

At Aays we understand that a comprehensive BI solution involves several layers that are not directly visible to Stakeholders and it is critical to have these processes properly implemented to get the best results. For example, at Aays we have divided this into the following layers:

  • Data Collections & Integration
  • Data Transformation
  • Data Governance
  • Data Modelling and
  • Data Visualization
  1. Data Collections & Integration: Organization generally have critical business information spread out in various systems viz. Cloud based data sources, ERP, excel sheets etc. It is important to aggregate all the critical business data into a centralized location via API calls, direct DB connects or other mechanism.
  2. Data Transformation: Data directly coming out of a system are generally in transactional form which may either need to be aggregated or transformed into a readable piece of information to be utilized by visualization tools. Furthermore, business rules that may needed to be applied on raw data also falls under Data Transformation.
  3. Data Governance: Designing fault tolerance, incremental refresh, and optimizing other aspects of data engineering are critical parameters to look at while designing a scalable and sustainable BI infrastructure
  4. Data Modelling: This refers to the logical inter-relationships and data flow between different data elements.
  5. Data Visualization: Finally, all the prior steps feed into a visualization tool to create reports, interactive dashboards for various stakeholders
  6. Embedding & Sharing of Report: Mature Organizations have thousands of reports and dashboards that are consumed by internal and external stakeholders. It is important to define report sharing logic (who should access what reports and what data), overall report access management and make such reports available via a user-friendly interface.
My Organization is primarily using Excel to get critical KPIs. Why do I need BI?

In the past few years, a lot of technological development has happened to enable users to see interactive reports with much more ease than ever before. While Excel is good for adhoc analysis, there are many limitations in terms of underlying data size, data governance, and overall presentation. If your Organization is more into Excel based reporting, analyze how much time your data analyst and other team members are spending in terms of cleaning data, transforming and then presenting to the key stakeholders and whether a transition to a BI solution would be more reasonable in terms of cost and benefit?

My company already has implemented ERP. Do I still need BI?

Traditionally ERP is supposed to capture all critical process in your organization and hence should be enough for your BI needs. However, this hardly is the case for modern organizations as the need is to have flexible reporting where end users can themselves drive reporting without much of IT support or ERP knowledge. For example, the ERP could be providing a lot of ‘off the shelf’ reports but the finance team / Analyst may need much more drill down in terms of understanding the data better. In such a scenario, analyst end up downloading data from ERP systems and essentially creating their own reports. Such a process is adhoc and does not result in a centralized management of data. Moreover, the underlying data in ERP system is in un-aggregated form and it might need transformation in order to be analyzed in a visualization tool. Recently, Salesforce (world’s leading CRM) acquired Tableau to enhance their BI and analytics offering. A lot of organization has already started using Visualization tools viz. Power BI, Tableau as an add-on over the ERP data but may missing the critical piece of data engineering viz. data transformation, data modelling etc.

We are a growth stage startup. Is it really the time to invest in BI?

As Startups are in their early phase of evolution, it is critical to create a data culture early on and leverage the same to create competitive edge. Several of our Clients (which are Startups) have leveraged BI competencies that has even contributed to their Valuations and that has commanded lot of enthusiasm among investors.

We are in the SME space. Is BI going to add any value on how I run my business?

Businesses are finding innovative ways to leverage data in their organization for several reasons. From identifying potential Customers to preventing frauds, SMEs are using BI to help them more effective.

If creating a platform for Analytics adoption is not among a CEO’s top two priorities over the next two to three years, that company runs the risk of becoming redundant. Companies that want to succeed in the future (especially SMEs) must master the radical transition by opening themselves to a journey that will change their organization models beyond recognition— the alternative being a catastrophic loss of market share and profitability.

I see a lot of talks about Data science (AI, ML). How is it different from BI?

Today Modern technologies like artificial intelligence, machine learning, data science have become the buzzwords. Everybody talks about but no one fully understands. Overall, Data Science, as used in business, is intrinsically data-driven, where many interdisciplinary sciences are applied together to extract meaning and insights from available business data, which is typically large and complex (See image below).

BI generally helps monitor the current state of business data to understand the historical performance of a business. Hence, BI is more of the building block for designing a more scalable and robust infrastructure for Data science. It is generally an Industry benchmark that every data scientist will need 4 data engineers (Data engineering is one of the building block of BI).

How can I go about building a BI infrastructure?

Every Organization needs the right resource and the culture to drive change especially when it comes to BI implementation. At first, an understanding of the current BI maturity level (in terms of Technology understanding, technology adoption, Talents/Resources) needs to be assessed and then accordingly steps need to be undertaken in order to reach to the desired vision. A detailed case needs to be built in terms of the following:

  1. What is the Minimum Viable Product (MVP) needed to meet business requirement? - For Many, it means different things. For example, a few may think of BI infrastructure to get better visualization and few may think of it an automation technique whereas others may think of it to drive critical insights.
  2. What are the desired components needed to build the MVP – For example: Is there any set data Integration work needed to centralize data. Should key departments prepare various business KPIs from a common data warehouse / datamart etc.?
  3. Should it all be in-house vs external consultants or SaaS products?
How do I assess my Organization’s BI maturity?

Every Organization needs the right resource and the culture to drive change especially when it comes to BI implementation. Broadly, we can categorize maturity levels as follows:

  1. Level 1

    Your business is analyzing data on an adhoc basis mostly in spreadsheets. Often spreadsheets are disconnected and there are no formal processes existing in the Organization to manage change, data etc.

  2. Level 2

    Business is undertaking BI and analytics projects in the company, but business units carry them out individually to optimize a process of their own or to make unit-specific decisions. There are no common practices / methodologies identified at a business level for data engineering, governance or having a centralized datamart etc. In this case, analytics are mainly driven by usage of visualization tools viz. Tableau, Power BI, Qlik from adhoc excel data dumps etc. In short, Analytics is mostly in the form of Data visualization sans any kind of data engineering and governance.

  3. Level 3

    There is active coordination between people, processes and technologies across the company and between various departments. Usually CIO / CTO undertakes such Projects with a team of BI developers and there exists a champion for BI usually from the ranks of business-side leaders.

  4. Level 4

    At this level, BI is sponsored by the senior management and the company actively invests in technologies and people to create competitive edge by data. In this case, there may be a C-level executive dedicated for BI and Data science.

When should I invest in BI?

The necessity of BI increases with the complexity of your business. Businesses in complex markets which have many different teams, systems, product lines, marketing channels and a diverse sales force should seriously consider establishing a business intelligence team. Depending on the type of company, the maturing of its product and infrastructure, and the amount of money in the bank, the company could consider investing in BI.

In-house team vis-à-vis SaaS Products vis-à-vis external consultants

As the focus on building BI infrastructure grows in the Organization, it is critical to determine what should be in-house and what should be out-sourced. It is a complex question, but this needs to be understood in the context of overall BI maturity. Here is a simple graphical representation and a cheat code in order to decide which will best suit for your organization.

  1. Off the Shelf (SaaS) Products

    Many companies selling off the shelf tools which can do many things viz. some companies are into integration of all sorts of data, and some are providing standard dashboards etc. These are available on monthly subscriptions, but this may server purpose only for Organizations which are in Level 1/level 2 in their maturity levels as these are usually not scalable and low cost.

  2. Companies providing PaaS or IaaS

    In this case, the responsibility is shared between Company and vendor as Data platform and other aspects resides with the company whereas the vendor simply manages the other aspects viz. Data visualization, integration etc.

  3. Tech Consultant building custom Analytics:

    Technology consultants are great, and they could be a great asset to helping your business develop custom Analytics. However, it is also important to have in-house understanding of the technologies to be used. For example, it is really important to assess

    • Which cloud technology to go for – Azure, Google Cloud or Amazon
    • Which ETL Tool should be selected
    • Which Visualization tool should be opted for – Power BI, Tableau, or others
How can I assess ROI on the investment made in BI?

BI solutions can be significant game-changers. To get closer to a determination of the actual value of the BI project, however, you must ask the following questions:

  1. Is there a right culture in the Organization to use BI in its fullest?
  2. What are the immediate tangible Financial benefits?
    • Can the reporting be automated to save staff costs?
    • Can we get more using BI and having right resources?
  3. What are the intangible benefits? Usually intangible benefits are the key driving force behind BI adoption – Ex: looking at the customer data or sales information to drive new revenue streams etc. or optimizing supply chain logistics cost etc.
Should I rather purchase off-the-shelf dashboards from SaaS Companies?

Depending on your BI maturity level, one can decide on this. Please refer question under “In-house team vis-à-vis SaaS Products vis-à-vis external consultants”

Should I rather build my own Infrastructure?

Depending on your BI maturity level, one can decide on this. Please refer question under “In-house team vis-à-vis SaaS Products vis-à-vis external consultants”

Should I rather hire Tech Consultants?

Depending on your BI maturity level, one can decide on this. Please refer question under “In-house team vis-à-vis SaaS Products vis-à-vis external consultants”

How Aays is different from Service Companies?

There are boutique Analytics firms which focusses on certain functional areas viz. Data engineering or Visualization etc. When it comes to BI implementation at mid-size companies, it is critical to have a unified approach and proposing a solution which meets business requirements. We don’t build everything from scratch as we have modules ready for agile and faster implementation. A typical service companies will implement everything from scratch, and we have ready-made modules. We will produce results quickly in weeks rather than months. Have successfully helped over 60+ Customers across 3+ continents so far in their BI Journey

Is my data really safe?

We follow the industry standards protocol and compliance needed from a cloud security perspective. Moreover, all your business data is maintained in the same jurisdiction and via a secured access (access is also IP enabled).

What Tech Stack Aays is currently supporting?

We are currently operating in the Microsoft technology Stack and are using Azure, Power BI for data engineering and visualization.

We already have BI implemented. What additional scope Aays can help us with?

Often BI implementation is treated as if it is a one-time implementation. Seldom it is the case as one would always need faster implementation for change in queries, changes in data model, performance monitoring, upgrades, and improvements to enhance BI adoption. We do provide Production support and maintenance of the existing BI scope and also churning out new insights.

How long does it take to implement a full BI solution?

Typically, any BI implementation is based on business requirement. The implementation of a full-scale BI would depend on many factors viz. number of data sources to be integrated, business transformation required, and number of dashboards needed. At Aays, our focus is to deliver results quickly and we start producing high level results in a week to two weeks’ time. A full-scale implementation may take anywhere from a month

If still wondering which plan is best for you