DATA MANAGEMENT

Analytics Hive renders advisory services in setting up competent Data Management Practices within Client Organizations to optimize Data Management operations, and subsequently hand over efciently-run operations to nominated champions in the client environment. Our depth and breadth of expertise capacitates us to tackle challenges at any stage of the data lifecycle shown below.Team Analytics Hive boasts battle-tested and distinguished Consultants who are DAMA-certied and entrech standards thereof in guiding and working with our clients to achieve set goals.

DATA MANAGEMENT METHODOLOGY

Goal: To improve operational efciency through effective management of (master) data elements for efcient use, aggregation, and distribution to business in the form of a long-term Data Management Programme

DATA MANAGEMENT APPROACH

Without the right quality data, the practical signicance of analytics is limited and often zero. Furthermore, data needs to be available to relevant audiences in a timely manner to support near-real-time decision-making required to stay abreast with client needs and getting ahead of the competition. We optimize Data Provisioning, Modeling, Application Development, and post- deployment monitoring of model-based solutions by harnessing the power of collaboration, agile, and lean practices characteristic of DataOps, ModelOps and DevOps.

HOW ANALYTICS HIVE EPITOMISES EXCELLENCE?

Data operations (DataOps) is the orchestration of people, process and technology to deliver trusted, high-quality data to data citizens fast. The practice is focused on enabling collaboration across an organization to drive agility, speed and new data initiatives at scale. Using the power of automation, DataOps is designed to solve challenges associated with inefciencies in accessing, preparing, integrating and making data available w h i l s t f o s t e r i n g p r i v a c y a n d compliance.

ModelOps is a holistic approach for rapidly and iteratively moving models through the analytics life cycle so they are deployed faster and deliver expected business value. ModelOps is based on t h e a p p l i c a t i o n d e v e l o p m e n t community's DevOps approach. But where DevOps focuses on application development, ModelOps focuses on getting models from the lab through validation, testing and deployment phases as quickly as possible, while ensuring quality results. It also focuses on ongoing monitoring and retraining of models to ensure peak performance

DevOps is the union of people, process, and products to enable continuous delivery of value to our end users. The contraction of “Dev” and “Ops” refers to replacing siloed Development and Operations to create multidisciplinary teams that now work together with shared and efcient practices and tools. Essential DevOps practices include agile planning, continuous integration, continuous delivery, and monitoring of applications.

DevOpsModelOps DataOps

Connected process excellence characterizes how we deliver automated insight-driven solutions

CONTINUOSMODEL

DEVELOPMENTAND DEPLOYMENT

CONTINUOSAPPLICATION

DEVELOPMENTAND DEPLOYMENT

DevOps ModelOps

Ingest data

Information in not a by-product of doing business; it is the lifeblood of business