Self-Serve Analytics – Actionable Data Visions
For easy understanding, businesses look at Self-serve Analytics, as Business Intelligence (BI) that lets every business User, fetch and create data insights for their own individual, custom use.
The ‘Self-serve analytics’ is seen by experts as BI tools that can be wielded directly by individual business Users, to search for their own custom data rather than relying fully on IT personnel. These BI tools, through easy-to-understand interfaces, let every business User browse through Enterprise databases and look out for the exact data they need rather than having to fetch them using complex technical scripts and SQL queries through technically qualified IT personnel.
The rise of Big Data, its related processing tools, technical scripts and SQL queries, have forced Enterprises across the globe to seriously view such data-sources as a potential gold-mine of valuable information. Enterprise Big Data are huge assets that have the potential to identify customer behavioral usage patterns, tendencies, and preferences. Enterprises view Big Data sources as total reliability elements that can drive a huge competitive edge and delightful customer experiences.
So, what is the bottleneck here?
It is extremely complicated and tedious for individual business User to probe through their exact treasure-data covered under disparate database systems and departmental silos. A recent Harvard Business Review has stated that disparate data disarrays and ambiguities are pinching the pockets of Enterprises up to almost &1.3 trillion.
Further, business Users devote a huge amount of their productive time searching their data from Big Data and devote even more time analyzing them, like searching for the needle in a haystack. These have become a huge pain in the neck for Enterprises, resulting in contradictions, affecting their productivity.
Self-serve Analytics (SSA)- the only ideal solution!
The huge wave of Self-serve Analytics(SSA) and BI tools have come in as a boon for Enterprises that have started empowering both business Users as well as knowledge workers.
Usage of tools such as Qlik, Tableau, etc. have made exponential pattern difference to the level of every individual business User, in quest of their ‘exact’ data.
Real-Agility features are the real need for SSA
Experts opine that Agility in the real sense, that can derive exponential productivity is the actual need for any Enterprise to invest in SSA:
- Simplicity of the SSA implemented, modeling innovative initiatives, and avoiding complicated analysis techniques go a long way in establishing business Agility.
- Individual business User-based, iterative, frequent implementations are the true expressions of customized needs.
- Sharing, collaborating within SSA implementation among Enterprise personnel account for perfect team-work.
- Business Process: SSA takes the corresponding individual Business User process for implementation, speeding up its operation as well eventually.
Ways business Users can discover ‘their own’ data through SSA
To make SSA truly drive true business value, Enterprises have to ensure the following end-to-end checklists:
- Enabling Data Trust through SSA tools, data accessibility, and data governance techniques.
- Empowered, insightful SSA
- Use of consumerization SSA techniques Data Catalogs creation
A deep-dive into establishing SSA methodologies!!!
1.Focus on establishing trust to implement dependable SSA
Trust through SSA Tools
Implementing SSA tools such as Qlik, Tableau, etc. facilitate both individual business Users and IT analysts to derive valuable, trustable insights with an intense focus on disparate data.
The catch here is individual business Users can operate on their own to churn out their desired data without even the slightest intervention from IT.
Trust through Role-based secured User accessibility to data
Accessibility to siloed departmental data as well as Enterprise Big Data to every individual personnel requiring them increases the trust and reliability they have on the disparate Enterprise Big Data.
Further, Role-based, advanced-encrypted data accessibility to individual business Users will surely establish security and protection to precious, sensitive, volatile enterprise data.
A typical example here would be, in a credit-card department of a Bank, developing a more comprehensive, customized User security tool with encrypted data (especially when it resides on Cloud). This not only secures crucial account and credit card related data from hackers but also increases the direct accessibility and trust that person would have on the data being accessed.
2. Empowered SSA coupled with trust for deeper insights
Empowerment and trust through Data Governance and Data Maps:
Understanding and establishing data trust by implementing Data Governance techniques, with data map integration of data from Enterprise’s disparate data sources, data journeys including their relationships.
Trust through proper Data Governance is a basic pre-requisite when it comes to voluminous Big Data analysis!!!
Data Governance is a comprehensive framework that enables Enterprises to establish clear-cut company policies, assets, and business rules. The special effects of systematic Data Governance are galore for great data definition and comprehension:
- User access to top quality data
- Seamless data inventory verifications
- Data Ownership establishment
- Identify Critical Data Elements (CDE)
- Determine data quality
- Ensure information safety
- Data retention and lineage maintenance
The new norm of millennial data governance establishes disparate data silos breakdown, collaboration, and understand its true value to churn-out competitive benefits. It is of utmost importance that Enterprise personnel completely trust this data for automated SSA and deriving useful insights through advanced ML techniques.
A typical SSA-Standards-Data Governance example
For instance, in conventional metrics approaches without the implementation of any SSA, a marketing analysis team may have multiple market analysts working on their own SSAs. There may be a business User-1 who may create a metric for average items in a basket, which creates two totally different types of reports for the same metric for an end User.
Implementing SSA for the same above scenario presents a totally elevated level of insightful report generation. In the SSA method, Users incorporate Data definitions, lineage standards within data dictionaries to define each of their metrics, including information on data element source and its changes. Such standards development facilitates reuse of the same metrics and avoids redundancies.
Training Users and establish KPI definitions
On one hand is training individual business Users initially on data usage for SSA, so that they can derive maximum insights from them. On the other hand, it is extremely vital that every personnel is, in line and agreement with the BI model KPIs. This is to ensure that every person possess adequate data visibility and understanding to make more informed decisions through SSA usage and to ensure no redundancies are performed such as unnecessary duplicate report generation.
3.Facilitate easy data consumption for SSA via Data Catalog creation
Data Catalogs creation as a sub-set of Data Governance facilitates:
- Simplified, strategic overview of data so that business users can simply pick from the list for usage in SSA
- Create consumable data for Users by organizing disparate data spread across geographical, departmental, and business group boundaries
- Facilitate easy trusted data shopping from a central repository that links business terms, glossaries, tables, and columns for a better understanding of its context
Mandatory Data Catalog capabilities
- Catalog with seamless data usability: Typical characteristic of a Data Catalog includes User-friendly data shopping interface, easy navigability with drag-drop functions, and understandable data along with their hierarchy and relationships as well as Help features. A typical illustration is an online catalog for a library. Such online catalogs are a mark of their sheer ease of use to quickly, priorly find the exact books that readers would want to hire from the library, even before visiting its premises. It facilitates readers to easily find books based on various data element parameters such as title, author, summary to name a few.
- Catalog in line with the business model: The purpose of a data catalog is to ensure easy, consumable data framework that is in line with all the operating models of the business. This operation model structure will link and align with source databases, business utilities, data lakes, quality systems, and metadata sources. The idea here is to establish an easily searchable, perfectly responsive system for the Data Catalog User with change auto-detection, and policy applications, creating KPI and definitions-based analytics models. A typical illustration for driving accurate insights and predictions would be, a marketing department business User who runs a Customer loyalty SAS report. Marketing business Users can derive useful consumer behavior insights from disparate sources such as website interaction data on one end and financial data from the backend systems.
- Personnel teamwork and collaboration: Apart from a precise algorithm, and business rule incorporation, a Data Catalog should ensure perfect collaboration between personnel’s operations. Business Users should be able to share data sets to save time on their creation avoiding redundancy. Further, document tagging and data set annotation increases the data value. This will greatly avoid confusion and need for huge data silos
- Trust: Business Users are further empowered with trusted data through Data Catalogs. This is because Data Catalogs are enriched with end-to-end business policies and rules, Data Governance protocols, Role-based data ownership assignments, etc. augment the trust of business users on the data they use. With the current global need for a regulated business environment, creating such trusted data and insights are extremely essential.
- Machine learning: The semantic search abilities of ML in the Data Catalog offers more pertinent data to Business Users over time. Eventually, data searches become more automated, enriched, and improvised that can be used for SSA. By incorporating machine learning functionality via semantic search capabilities, the catalog can serve up increasingly relevant data to users over time and offer an automated and efficient way to improve data searches to be used in the analysis. This is the Amazon-like feature mentioned earlier.
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