We have performed extensive research and implemented effective solutions in the field of Public, Private & Hybrid cloud computing.
Our Big Data practice team provide efficient and cost effective techniques to harness your Big Data platform with optimal performance.
Data Governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information.
We provide best Archiving techniques utilizing the tools available in the market.
We offer best in class IT Road-maps by analyzing your Organization current State and provide multiple paths to achieve your future State.
We understand your data security is the top priority. To address your security needs, we have designed most powerful & flexible security models to protect and support your data needs.
Key Industry Players who once only developed Software have realized that by integrating the Software, Platform and Infrastructure combined together, they can offer their customers superior value and technology finesse to ensure the Software performs it’s intended deliverable in the ideal conditions. While several cloud players exist in the market, the key goals to consider Cloud is to not just reduce dependency on internal servers / patching / keeping up to the ever demands of the Software Vendor, but Cloud Systems offer resilience, backup strategy, most up to date Software Version and above all, an integrated rich user experience that helps business achieve their strategic goals. The recommendation is to consider Cloud as an all-pervasive center of excellence platform and need to be part of the overall road-map towards BI excellence.
By using cloud computing, BIORBIT can achieve a lower variable cost compared to cost achieved on your own. Due to usage from hundreds of thousands of consumers being aggregated in the cloud, service providers can achieve higher economies of scale which translates into lower pay-as-you-go prices
Eliminate guesswork on the infrastructure capacity needs. When a capacity decision is made prior to deploying an application, it is often either sitting on expensive idle resources or dealing with limited capacity. With cloud computing, these problems go away as access can be as much or as little as needed, and can also be scaled up and down as required with only a few minutes notice.
In a cloud computing environment, new IT resources are only a click away, which means the time it takes to make those resources available to the developers is reduced from weeks to minutes. The result is a dramatic increase in agility for the organization since the cost and time it takes to experiment and develop is significantly lower.
It highlights the ability to start focusing on projects that differentiate the business, not the infrastructure. Cloud computing focuses concentration on the consumers, rather than on the heavy lifting of racking, stacking and powering servers.
It allows easy deployment of application(s) in multiple regions around the world with just a few clicks. This means a lower latency and better experience for consumers at a minimal cost.
Our approach to data structure and analytics are different than traditional information architectures. A traditional data warehouse approach expects the data to undergo standardized ETL processes and eventually map into pre-defined schema, also known as “schema on write”. A criticism of the traditional approach is the lengthy process to make changes to the pre-defined schema. One aspect of the appeal of Big Data is that the data can be captured without requiring a ‘defined’ data structure. Rather, the structure will be derived either from the data itself or through other algorithmic process, also known as “schema on read”. This approach is supported by new low-cost, in-memory parallel processing hardware/software architectures, such as HDFS/Hadoop and Spark.
A Data Lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a Data Lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question.
The term Data Lake is often associated with Hadoop oriented object storage. In such a scenario, an organization’s data is first loaded into the Hadoop platform, and then business analytics and data mining tools are applied to the data where it resides on Hadoop’s cluster nodes of commodity computers.
TIBCO’s SpotFire is an analytics and BI platform for analysis of data predictive and complex statistics. It allows us to create, manipulate, and deploy rich analytical visualizations, learning about our data and letting us get to insights quicker – regardless of whether we are an experienced analyst or a new user to visual analytics. The Big Data figure above represents the implementation of Big Data with the combination of Data Lake, Hadoop and SpotFire. It uses NoSQL columnar database to store the data.
As part of introducing the big data platform for few of our clients, We recommended taking iterative approach to initiate this modern data platform.
Set up Big Data platform in cloud environment
Execute the Big Data POC for a specific financial use case
Potential use case to considered for doing the big data POC is for Audit Analytics.
Audit analytics is an analytical process by which insights are extracted from operational, financial, and other forms of electronic data internal or external to the organization. These insights can be historical, real-time, or predictive and can also be risk-focused (e.g., controls effectiveness, fraud, waste, abuse, policy/regulatory noncompliance) or performance-focused (e.g., increased sales, decreased costs, improved profitability) and frequently provide the “how?” and “why?” answers.
Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. Big data platform enables building effective predictive analytics solution by analyzing all required data from a single data lake repository. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.
Data Governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of data and information in enabling an organization to achieve its goals.
The main goals and objectives of data governance include:
Define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics
To track and enforce conformance to data policies, standards, architecture and procedures
To sponsor, track, and oversee the delivery of data management projects and services
To manage and resolve data related issues.
To understand and promote the value of data assets
Determine enterprise data needs and data strategy
Understand and asses current state data management maturity level
Establish future state data management capability
Establish data professional roles and organizations
Develop and approve data policies, standards, and procedures.
Plan and sponsor data management projects and services
Establish data asset value and associated costs.
Coordinate data governance activities
Mange and resolve data related issues
Monitor and enforce conformance with data policies, standards, and architecture
Communicate and promote the value of data assets
Data management cost
Achievement of objectives
Number of decisions made
Steward representation and coverage
Data Professional headcount
Data management process maturity
To make the right business and operational decisions about data, it is important to determine which data is cold, which data is warm, and which data is hot. Data that is frequently accessed on fast storage is considered hot data, less-frequently accessed data stored on slower storage is warm data, and data that is rarely accessed and resides on slowest storage is considered cold data.
It is vital that to understand how it is used, both in terms of the workload and in terms of the capacity that in the data warehouse. To successfully rebalance the data warehouse, the first step is to access and identify less-frequently accessed data and resource-intensive workloads.
For example, a typical data warehouse may have hot data that is using 50% of the CPU cycle, which is busy performing data transformation and extractions. Often this kind of workload is consuming only 10% of the total data warehouse workload, therefore, that 10% of the data warehouse workload is using 50% of system CPU cycle. That is the hot data! This scenario where organizations using the data warehouse to do a lot of workloads which may be better suited for a different platform – is quite common. It is not just about hot, warm, or cold data, it is about understanding where the data is, when and how it is being used, who is using it, and what the organization wants to do with it.
Articles – Good topics for articles include anything related to your company – recent changes to operations, the latest company softball game – or the industry you’re in. General business trends (think national and even international) are great article fodder, too.
Mission statements – You can tell a lot about a company by its mission statement. Don’t have one? Now might be a good time to create one and post it here. A good mission statement tells you what drives a company to do what it does.
Company policies – Are there company policies that are particularly important to your business? Perhaps your unlimited paternity/maternity leave policy has endeared you to employees across the company. This is a good place to talk about that.
Executive profiles – A company is only as strong as its executive leadership. This is a good place to show off who’s occupying the corner offices. Write a nice bio about each executive that includes what they do, how long they’ve been at it, and what got them to where they are.
Once there is an understanding about how the data is being used, both in terms of overall workload and data warehouse usage, the next step is to record that information in the context of user activity. When aspects have been identified, the development of the road-map for moving the data can begin. A holistic approach to creating the road-map for data movement would be to:
Identify the workload(s) that need to be moved
Scope out how to move that workload
Understand how the different applications and databases are connected to each other
Determine architectural investments that need to be made
It is necessary to understand that optimizing data warehouse needs to be done iteratively – again and again – because data is dynamic and changes over time. Data that is identified as hot one month/year may be considered warm or cold next month/year.
Our security experts team has developed most powerful and flexible security model to support your Organization data. A security policy outlines how data is accessed, what level of security is required and what actions should be taken when these requirements are not met. The policy outlines the expectations of an application or system as a whole. A security model is a statement that outlines the requirements necessary to properly support and implement a certain security policy. If a security policy dictates that all users must be identified, authenticated and authorized before accessing network resources, the security model might lay out an access control matrix that should be constructed so that it fulfills the requirements of the security policy. If a security policy states that no one from a lower security level should be able to view or modify information at a higher security level, the supporting security model will outline the necessary logic and rules that need to be implemented to ensure that under no circumstances can a lower-level subject access a higher-level object in an authorized manner. In the security model, implementation of new modules will be critical to design data authorization security.
The following are the basic guidelines we use for implementing an ideal security model:
Module level permissions
Sub module-level permissions
Field level permissions Roles
Data sharing rules Users
Assign profile and role to user Groups
Associate users and roles to group
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