Knowledge Hub

Data Literacy is the key for all Data Initiatives.

We know the difference between Business Intelligence (BI) developers and Business Analysts (BA). In previous eras, BA used to talk to business, create KPI(s) and pass it to BI developers to create reports and dashboards. 90% of BI developers would not know what a report or a dashboard is for. They won’t know the impact of good and bad reports. They won’t know the ROI of information this report, or dashboard can produce due to which they don’t care whether BA has provided right KPI(s) or not. This era was not a Data Era, in that era Reports or Dashboards were good to have. Most of the decision making used to happen in reports extracted in excels 😊.

Now time has changed, we have entered the Analytics era as compared to previous era of Analysis. Difference between Analysis and Analytics is simple but tricky i.e., Analysis is about finding Whys’ and What’s whereas Analytics is about finding How’s from a dataset. In other words, in datasets, Analysis search Questions and Analytics propose Answers.

Data Driven Organization is a new Buzz word in market where every organization wants to tag this Key Word with their organization.

Traditionally, we use data to make decisions. Decisions at top level for organizational revenue generation planning or at division level like whether division of a particular product is performing well or is it achieving its planned ROI, is it generating profits for the company.

Above mentioned attributes or properties doesn’t make you Data Driven Organization, above are previous era organizational behavior. Now, the current era is all about Data, everything within an organization must follow data decisions. When we data decisions we are talking about Daily Chores. We are talking about how employees hiring, and firing is done, we are talking about how transfers are done, we are talking about how printer Ink or Papers are orders, we are talking about procurement works, we are talking about how hardware, software or infrastructure resources are provisioned or de-provision.

Data Entitlement is normally mixed with Data Authorization which is not 100% accurate. There is no doubt, there are few places where both can be used interchangeably but mostly, they should not.

Entitlement is something which you inherit, or which comes as a default or which you get along-with something else whereas Authorization comes because of an action, or it expects an action in future. For example, you are ENTITLED to something and at the same time you are AUTHORIZED to do X, Y and Z things.

Before we understand the difference between Authorization and Entitlement, it’s very importance to understand what Prescriptive and Descriptive behaviors are.

• Prescriptive means, how it should be, and an action is expected.
• Descriptive means, how it is.

‘Authorization has a Prescriptive behavior whereas Entitlement has Descriptive behavior.’

Let’s try to understand with an example, at work you must have heard, you are ENTITLED to 20 annual leaves and maybe 12 medical leaves etc. But when you apply for a leave, your manager has the AUTHORITY to accept or reject your leave.

Authentication is the basic security feature of any organization. An organization without Authentication is like gold mine without any guard.

Authentication and Authorization works hands-in-hand and are a MUST-TO-HAVE in any organization or in any system or in any application.

Authentication is majorly tagged with Security whereas Authorization is majorly tagged with Privacy.

Let’s try to understand with an example, for example, in a hospital close relative, family or friends are allowed to come and meet the patient, but all these people normally are AUTHENTICATED or stopped by the guard at the main gate who checks their ID cards or anything which can assure that they are legitimate people e.g., guard can ask, why they are here etc. Once they are in, at the main hospital counter someone again asks them who they want to meet, if the patient’s name is there in system, then they are allowed to go ahead.

Self-Service and Data Enrichment are mostly confused with each other where data enrichment is just one part of one of the types of self-services.

Self-Service is about empowering business users and decision makers to conduct activities by themselves to shorten the end-to-end delivery time. At the same time, it’s too risky to allow people to go and make changes if they don’t understand data. So, I would say, Data Democratization is a pre-requisite for any type of Self-Service. There are many types, but we will only discuss below.

Types

• Data Self-Service: In this kind of self-service capability, data friendly environment is made available for users, where users are trained on data, users are allowed to question data accuracy and integrity, users are allowed to manipulate and extend the context of data via Data Enrichment process where first-hand data is enriched with second-hand and third-party datasets for better decision making.
• Analytics Self-Service (data citizen): This self-service mostly involves machine learning, data modelling or in other word data science related activities. Data Citizen are major users of this self-service where they run data science or data mining models using low or no code tools. Data Enrichment can also be done using this self-service capability.
• BI Self-Service: As the name explains itself, the users are allowed to connect to any source systems either internal or external, of course based on the access level, and create reports and dashboards on their own. This is majorly to reduce the dependency on IT to create reports for business users. In this era, the paradigm of reports and dashboards creation has moved from IT to Business users.

Business Intelligence and Business Analytics both look the same but have different standing in terms of Decision Making.

Business Intelligence has been there for decades with respect to tools and technologies. Business Analytics has also been there but mostly tagged with manual effort by Functional, Business or Financial Consultants.

Howard Dresner coined the modern definition of the term “business intelligence” in 1989, at least in the sense it is typically used in industry today (“end-user access to and analysis of structured content, i.e., data”).

As there weren't a lot of tools available in the market for Analytics, so many companies were making fortunes for this piece of manual effort. Now the question comes, when Business Intelligence tools were there, how come Business Analytics was manual and why Intelligence and Analytics cannot be done using the same tools? For this, one must first understand the differences in how Intelligence and Analytics processing works.

We have covered Business Intelligence, Business Analytics, Data Modelling and Data Cardinality, it's the right time to discuss Data Visualization.

Data Visualization is what is seen by End Users. The Success or Failure of the project is determined based on it. As the phrase goes, 'If you can't draw it, you don't Know it.' And it’s very true. One can spend millions of dollars, can implement a perfect Data Strategy, can have perfect EDW or Data Lake etc. etc. but after all the hard work if the End User can't see what is implementation, if the End User is unable to make use of the data for DSS, if End User will have to go back to their Finance or Technical departments for manual data extraction then all the efforts put-in is nothing more than Waste of Time and Dollars.

Now, there are rules to have good Data Visualization which is relevant to any visualization tools in the market like Tableau, Qlik, Power BI, OBIEE, Cognos and many more.

These terms are the core of the Data Pipeline and are normally confused by each other.

Data Integration is to connect with one or more source systems to pull data whereas Data Blending is to merge all datasets into one viewable and understandable presentation view to answer business KPIs. In other words, Data Integration is the prerequisite process for Data Blending, if you can't get data from source systems, you can't blend it to present management for decision making.

Don't forget either Data Science, Business Intelligence or any kind of Analytics all work toward ONE BIG FAT REALITY, to achieve one or the other Business KPI(s).

Coming back to blending and integration. Don't forget in an organization, we have many systems with different objectives where any ONE kind of data could be residing in different patterns in different systems. For example, a product can be sold via multiple channels like from a shop or an online website or maybe from a mobile application etc. When product data is pulled from all these systems in one Data Warehouse solution to report product selling behavior, it's of utmost importance to gather all product selling transactions from all systems into one system which in our example is a Data Warehouse.

Data Mashup is normally confused with Data Mesh (explained separately), but both are totally different.

Data Mashup comes from the idea of doing self-service on a single source of truth. Dashboard or a Web-Page data point coming from multiple sources in a single view is called Data Mashup. Yes, nowadays, we all do this daily.

We have dashboards with data coming from multiple sources which can be Databases, Data Lakes, social media etc. In the Big Data era, we have graphs, pictures, videos all on one page. At the organization level it’s also called Enterprise Mashup. It is also very useful for Data Analysts to fetch data from different sources, make sense of the data and build meaningful data for business decision-making.

Data Harmonization is normally confused with Data Integration, Data Blending, Data Warehousing, Data Marts etc. Yes, all do the same steps of connecting and bring all required data from all the disparate sources in one central location, but Data Harmonization goes one step ahead.

Data Harmonization standardizes data coming from multiple source systems in one unified format, so it becomes a bridge between data and business requirement. When we say, one unified format, it means data is converted in such a way that it can become accurate for someone.

Data Harmonization can be of many types.

• Harmonizing data in one location for a specific department can be called a Data Mart.
• Defining standard naming conventions, data type, codes coming from multiple sources in one unique format can be Harmonizing data for Master Data Management.
• Gathering Metadata details of all source systems, data lakes, data warehouses, data marts up to reports and dashboards is Harmonizing data for Data Lineage.