More about theServices We Provide

The following services represent areas where BIEXA have primary focus and technology skills. Delivery can be by BIEXA directly - typically during a POC initiative, - or as a long term transfer of knowledge/enablement of your Business Intelligence and Machine Learning team.

Data Warehousing

Historically Data Warehousing technologies come with high cost and high complexity. Cloud is in some degree solving this problem, but not entirely.  As a decision maker, you should understand the fundamental difference in the way in which that these services are delivered and how this impact your Total Cost of Ownership.

The Google BigQuery platform is a true fully managed, no-operations data warehouse. The concept of hardware is completely abstracted away from the user and it comes with the necessary native integrations with many third-party reporting and BI providers such as Tableau, MicroStrategy, Looker, Clik Sence and so on. 

Spend less: By "lift & shift" of existing Business Intelligence platforms for DBMS and ETL (like TeraData, Oracle Essbase/Exadata, IBM Informix/Netezza and Microsoft SQL) to GCP, companies will be able to reduce their TCO by af factor 4-5. By selecting Google BigData the potential is from 20% to 50 % below alternative cloud platforms and architectures (larger datasets = larger saving)

Pay for use: The cost of compute (storage, CPU and I.O.) is significantly lower than any existing on-premise architecture. You will not be bound by contracts on SW and hosting - the cloud offers access to data warehousing and streaming platforms without investment in licenses. This brings absolute agility to your innovation projects and enables your to work truly agile when remodelling e.g. the full datamodel in your BigData installation. I.e. no need to procure Entreprise licenses up front, or to pre-allocate compute capacities in e.g. a Hadoop cluster that you are not able to fully utilise.

No-ops: In contrary to all other available on-premise and cloud offerings - GCP is determined to eliminate the need for dev-ops. I.e. you focus on business value and agility in developing your analytics or IoT initiatives.

Flexibility: When working with BI development in classical on premise installations - the development constraint has always been access to performance and capacity in your tiered development environment. Cut all risk when e.g. redesigning your entire data load and column based datamodel - by validating your sprints on the full data-set and with full load simulation.

Scalability: The Google BigData platform is commercially available with "state of art" architecture that support Google core offering. Get access to infinitite autoscaling on all dimensions. Build on the same technologies that powers the worlds largest search engine.

Reliability: Run on the most secure platform in the world. Google employ more that 600 of the best security technicians in the world to ensure that their cloud offerings and own core operations is safe.

Data Processing

Did you know that Google created BigData, Pub/Sub as their veichle, to load all information from the internet (all of it) into their column based database, for subsequent indexing and to support their search business. It is this architecture and tool set, that is now commercially available for corporations to adopt to build solutions in. The potential of moving you classic batch ETL to real time processing by streaming data ingestions directly through an API or by using Google Cloud Dataflow is significant. Without processing bottlenecks in both batch and streaming , you are able to support disruptive predictive analytics and new and faster business models (e.g. IoT or CRM).

Many enterprises are struggling with performance in their existing Data Warehouse. Typically this is caused by processing constraints that caused architects to replicate data models from existing OLTP databases, coupled with expensive single row, primary key querying. Google Big Data is not the right tool for single row querying. But if you adopt a column based paradigm during implementation, where the data model is  flattened out and de-normalised before storing it in BigQuery, it is possible to take full advantage of the analytics engine. Extremely fast querying on petabyte scale thru the highest data compression available is the promise.

Seeing is believing!  We recommend a PoC to demo the raw power and to provide you with solid estimates of capacity and implementation costs.

Data Insights

Machine learning (ML) has the power to greatly simplify our lives. Improvements in computer vision and natural language processing help all of us interact more naturally with technology. Businesses rely on ML to strengthen network security and reduce fraud. Advances in medical imaging enabled by ML can increase the accuracy of medical diagnoses and expand access to care, ultimately saving lives.

Enterprises that are looking for classical optimisation tools on top of their ERP systems should evaluate if the better option is to build it using existing available general purpose frameworks. Google Online Prediction supports multiple frameworks to serve: classification, regression, clustering, and dimensionality reduction models

  • scikit-learn for the breadth and simplicity of classical machine learning
  • XGBoost for the ease and accuracy of extreme gradient boosting
  • Keras for easy and fast prototyping of deep learning
  • TensorFlow for the cutting edge power of deep learning

By moving your compute to GCP you will achieve true convergence with interoperability from raw data to any purposeful meaning that you may desire (reporting, workflow, machine learning, end user apps, IoT actions, social media etc. And you will Gain benefits from the fastest growing toolset in ML (sentiment, vision (image and video), natural language processing and speech.

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