Acquiring data for analysis can be problematic due to resource constraints, unpredictable sources or a knowledge gap of the data sources. MoData has designed to address these obstacles accepting all likely formats from a broad range of sources with minimal technical knowlege.
Data can be supplied as file extracts from databases or data-warehouses, database dumps, MS Excel workbooks, delimited files, production reports, unstructured text or streamed sensor data.
There is no need to install a local data structure - files can be uploaded to your own FTP or sFTP site, Box or Dropbox or files can be directly uploaded from your desktop.
As the files are read in, their type is recognized, the file is checked for encoding errors and corrected (manually or with assistance from a smart agent). The initial read and parse will quickly determine primary and secondary keys, recognize field types and values in the data. The data is now available for discovery.
A task that might take days or weeks for large numbers of files can now be performed in minutes and hours.
Once data sources are available, there is a mundane and time-consuming task of file analysis, that is understanding the full dimensionality of the data and then quickly honing in on where to begin an investigation.
Today this process relies on brute force before being able to apply analytic experience and domain knowledge. MoData makes quick work of this painful process of gaining a true understanding of structured, semi-structured and unstructured data.
MoData will carry out the initial high volume analysis, indexing, search and reporting on both meta-data and the file content, reporting on simple file attributes, examining data quality, performing entity analysis, suggesting correlations and anomalies in the most significant entities.
This process will inform information governance and provides the pre-requisite to any data science project.
Cleansing, transforming aligning and de-normalizing data is another chore, also a lengthy and largely manual process. Data sources are rarely clean and almost never easily mapped to each other. This process of architecting an analysis-ready structure with transformations and aggregations has always taken time and effort. MoData provides fast automation leaving data ready for analysis.
The Data Publisher is a self-service data integration that automates the high volume data loading, cleansing and enrichment while building a viable data structure for analysis.
Both IT and analyst users are able to curate this process quickly and efficiently by simply editing the operations created during the data exploration process.
Once the analysis task is complete, delivering that insight to a large population of business users can also be tricky. Desktop or cloud based analytic products present access challenges requiring IT support and are often overkill for business users who want critical insights, delivered simply.
MoData provides skilled analysts with their own SQL- and R- command line interface to the granular data, export to CSV or direct access via third party visualization tools. Futher, it will allow the creation of anything from a mobile widget to a full dashboard that addresses the needs of the business user.
For developers, the Insight Portal allows charts and dashboards to be quickly created with simple Java-based widgets and an integration with LDAP directories allows roles and permissions to be created and managed
Leading management thinkers are now recognizing the importance of a Data Strategy as they streamline business processes and consider new systems infrastructure. Data is a central (yet distributed) corporate asset that opens opportunities to supply cross-functional insights to the business to optimize operations and create whole new lines of business based on the data.
The Data Strategy study and report underpins future execution projects that directly address the problems and possibilitiesfor data across the entire organization. It delivers the executive mandate that alignsstakeholders around data as a valuable business asset.
For downstream systems to perform, they rely on their data inputs being accurate and timely. As the sources of available data increase, data quality becomes a concern and exposes flaw in processes and governance.
The Data Quality Project is a pragmatic yet long term solution to the issues regarding data quality beginning with a health-check and action plan Data sources, in particular Master Data related processes must be cleansed against a gold-standard. Processes will be established to ensure that new data received from other systems or from external parties is automatically checked and cleaned.
Governance will be established and as new data sources are brought on board, that the data created to standard and that feeds will be timely and correct
Making the optimal business decision, whether at a strategic or an operational level, requires information - and that information needs to be timely, accurate and delivered at the right level. The raw data used to generate actionable information can live in different systems, different functions and even different companies (i.e. outside the organization). Today, new insights will deliver a competitive advantage - and those insights will be delivered out of data as the raw material.
Data is not only an abundant raw material, but it is also one that can be captured or generated in new ways. The insights gained from data, especially combining the data inside the organization with external data such as geo-spatial or public domain data, can lead to entirely new lines of business. The asset is potentially valuable and game changing, however, it is easy to get sidetracked and make huge investments in data infrastructure or analytics that do not pay off.
A ‘data product' is the processed data deliverable that creates business value. Inside the organization these may be reports, dashboards or data feeds that go into process control or the insights that drive criteria for new avenues of exploration. Data products need a unique type of product management, one that reaches across technologies and markets to deliver the value hidden in the data.
A team of highly qualified consultants leverage advanced analytics tools and techniques coupled with broad domain expertise to solve complex business problems.
We cater to some of the largest Fortune 500 companies across different industries including Mining and Manufacturing, Financial Services, Hi-Tech and CPG.
MoData is on a mission to make complicated data simple for business people.
Our founders have spent their careers turning Enterprise data into food for hungry decision makers. Deeply technical, they understand what really goes on behind the data warehouse, how data should be governed and how to quickly process dirty, unstructured or streamed data sources on the cloud. Decades of experience mean they know how enterprises really tick, the pressure that the IT department works under and how business users always need more data to make decisions.
In 2014 - armed with all this knowledge, and with a vision of taking raw data from any source and automatically turning that into valuable business insights - they created MoData. The MoData platform is superior technology that accelerates how businesses can turn data into value. We're headquartered in Silicon Valley and have the smartest engineers and data scientists busy in San Francisco and an increasing number in India and Bulgaria. We've worked with some of the largest organizations in the world, helping them drive profitability, create competitive advantage and save money.
At MoData, we know data. From identifying valuable data sources to cleansing and normalizing your data all they way through creating data products that you can deploy throughout your organization, MoData is all about turning big data into a big asset.
Let us help you ask better questions.