Managed Analytics and Data Analysis Services
ScienceSoft is a reliable outsourcing partner for companies that want to get insights from their data while skipping the technicalities. We take care of the data analytics infrastructure and apply advanced data analysis to provide our customers with regular and ad hoc reports, alerts, and predictions, as well as self-service analytics. As a result, the companies that make use of our managed analytics services can prioritize on accurate planning of their activities, ongoing business management, change management, and optimization of their business processes.
- During 30 years in data analytics, we have built up comprehensive competencies that cover traditional and big data, data management, business intelligence (including self-service BI and data visualization), advanced data analytics, and data science.
- We partner with Microsoft, Amazon and Oracle. 2 of 7 Microsoft Gold competencies we hold acknowledge the expertise of our data analytics team – Data Analytics and Data Platform.
- We have substantial domain expertise, our focal industries being manufacturing, retail and wholesale, professional services, healthcare, financial services, telecommunications.
- In 2018, we were rated A+ by BBB, recognized as the top IT company by Clutch, as well as featured on The Silicon Review listing among 10 Fastest Growing Data Analytics Companies.
Types of Managed Analytics Service We Offer
For those customers who have tons of raw data to derive insights from but don’t plan to invest into constructing a full-scale analytical solution and supporting it, we offer managed analytics services on a monthly subscription fee basis.
The subscription fee covers:
- Data management activities, such as master data and metadata management, data quality assurance and data security.
- Regular reports, including the ones powered with advanced analytics.
- Access to self-service analytics.
- The agreed number of ad hoc reports of the specified complexity.
- Alerting of an agreed type, for instance, alerts requiring attention or urgent actions.
- Regular tuning of machine learning models, if such models are used.
Extra activities and services are covered based on the time-and-materials (T&M) basis.
To those customers who already have a centralized analytical solution but don’t have time or resources to upgrade it to satisfy the specific analytical needs of a particular department, we suggest outsourcing the uncovered part of their data analytics to us. In this case, in addition to the same service as with complete outsourcing, we ensure the integration of our data analytics infrastructure with the customer’s central data warehouse and closely collaborate with the customer’s support team.
At this stage, we analyze the customer needs and as-is situation: existing data and data quality practices, as well as the analytical solution, if any. We examine the company’s business plan and collect input from IT and business departments in order to understand the customer’s analytical needs. Based on the findings of the discovery stage, we plan the service and agree on the service level agreement (SLA) terms with the customer.
We extract the data into a data warehouse, clean it to ensure that the data is of high quality, integrate with the customer’s data warehouse, if any, create OLAP cubes for exploratory analysis and train machine learning models, if advanced data analysis is required. This is the stage where the responsibility transfer takes place.
Responsibility we take is high: we deliver value though analytics having high freedom in process and resources:
- We are responsible for setting up processes and managing them. The customer may or may not be involved in approving substantial aspects.
- We are responsible for allocating and managing proper resources. The customer may or may not be involved in approving them.
We provide access to self-service analytics tools, deliver regular reporting, as well as ad hoc analytics upon the customer’s request, which is the foundation for data-driven insights. If required, we set up alerts for business users that notify if any anomaly is detected in analyzed data or a certain threshold is reached. We can also deliver accurate forecasts that will become the basis for optimizing a company’s internal processes.
We are agile and we adjust to the customer’s changing business needs to provide relevant reporting. For instance, if the need arises, we can add data sources, both internal and external – say, to enrich the internal sales data with the findings of the recent external research on the industry performance data, for example, with the revenue per salesperson. Besides, we constantly work to increase the quality of data analysis. For example, to improve the accuracy of predictions, we retrain the machine learning models based on a larger pool of historical data that has become available.
Data we analyze
As a data analysis company, we analyze various types of internal and external data:
Analysis methods we apply
Our Approach to Collaboration
With the aim to minimize management efforts on the customer’s side and stay transparent, as well as satisfy business needs in analytics within budget and technical limitations, we establish friendly and result-oriented collaboration with the customer and, if required, with third-party vendors, such as the vendors supporting source systems, managed service providers, and managed security service providers.
For example, we assign an account manager to stay in touch with the customer’s contract manager or the manager of the entire outsourcing project to agree on the budget changes, adjust the SLA, if needed, review KPIs and analyze user satisfaction. If a problem is unsolvable on the current level, we escalate it appropriately.
Here’s a detailed collaboration matrix:
Our analysts stay in touch with the customer throughout the entire cooperation to adapt the service we deliver to the changing needs and requirements. We apply an agile approach so that analytics users could quickly check their ideas and make data-based decisions, as well as a proactive approach to foster data-driven decision making.
The entire collaboration is based on KPIs that cover such important aspects of data analysis as quality, ad hoc analytics, user satisfaction and security. The KPI set may look as follows:
- The availability of the service to business users.
- Timeliness of the report updates.
- Alert precision (alerting on significant events).
- The number of ad hoc reports by complexity.
- User satisfaction score for each user type – analytics consumers, explorers and analysts.
- Vulnerability assessment results.
The status of these KPIs is reflected in monthly reports that we provide the customers with.
Never Mind the Technicalities, Get Insights
Share your analytical needs with us, and we’ll plan the service for you. Let’s start with a free consultation – our team of proficient analysts will be happy to answer the questions you may have.