The average Datalore deal is €35,000 per year, it is very easy to identify the right end customer with the highest probability of purchase, and we at QBS are at your side with dedicated product managers to prepare the deal and finalise it quickly.
JetBrains Datalore is the private platform for your team for collaborative no-code analyses and scalable infrastructure. It is a collaborative data science platform. It streamlines insight delivery and makes data and business teams more productive together.
Datalore combines:
- Powerful coding assistance for Python, SQL, R and Scala in Jupyter
- Data integrations for SQL databases and cloud storage
- Modern business intelligence and interactive data
- Real-time collaboration on code and in team
- AI for accelerating and facilitating the work of data analysts and tech-savvy business
Target Audience
- Core Data Teams: Data scientists and Data
- Business teams that need to make data-driven decisions: Business analysts, Marketing analysts, Sales analysts etc.
Core Data Team
- Use Python or R as primary programming language
- with Jupyter notebooks at least 40% of the time
Optional characteristics (the more team has – the better the fit):
- do reporting in Tableau, PowerBi, Spreadsheets, Streamlit, Plotly dash or other tools
- are using SQL for data retrieval and use DB querying tools
- Team works with multiple data sources (databases, buckets, files, mounted file directories, 3d-party APIs)
- team is remote or distributed
- the company has a strict data security policy and can’t upload data to 3d party services
Decision makers:
Data Science team leads, Data analytics team leads, Head of Analytics, Head of Data, Head of Data Science, VP of Engineering, Chief solutions architects, IT Director, Head of DevOps, CTO
Business Team
- Sales & Marketing teams that can’t code, but need reporting and
- Product teams that can code with SQL, but mostly need reporting and
- External and internal stakeholders that need reporting and dashboards.
Decision makers:
Head of business functions (Head of marketing, Head of sales, Head of business analytics), Head of Business Intelligence, C-levels
Key problems Datalore potential customers face:
Core Data Team
Repetitive actions and boilerplate code
- Setting up team database access. SQL queries need repetitive code; no auto-completion.
- Need to learn library syntax for tasks like visualization and dataframes, leading to frequent documentation
- No code-completion
Collaboration inside and outside of the data team is problematic
- Sharing Files, Notebooks, Data Connectors, and Reports across the team is hard to set up and
- Real-time collaboration on the notebooks is absent.
- Local setups are impossible to scale and govern.
Large “Zoo of tools” and context-switching
- The data team utilizes Jupyter, Various plugins, Data sources, Airflow for scheduling, and Streamlit, or Plotly for report deployment and sharing, leading to context switching for users and increased maintenance costs for companies.
Sharing analytics results with stakeholders is time-consuming.
- Notebooks are not suitable for presenting results to non-technical
- Creating and distributing presentations from notebooks is a time-consuming repetitive task.
Business Team
Problematic collaboration with the Data team
- The data team frequently acts as a bottleneck, delaying tech-savvy business teams.
- No accessible tooling for self-service analytics by citizen data scientists.
Need for a more comprehensive analytics tool despite insufficient coding skills
- Fear of adopting new analytics tools because of the complexity and coding skills required
- Require reports featuring a drill-down option.
Value Datalore brings to its customers:
Core Data Team
Improving team’s productivity:
- Streamlined data connectors’ sharing in several clicks and SQL cells.
- Advanced code completion from PyCharm and seamless real-time collaboration across notebooks, workspaces, and reports.
- AI-assisted code generation and analysis suggestions, alongside no-code features like chart builders and interactive tables for user-friendly data interaction between business users and data teams.
Improving velocity of insights delivery
- Quickly connect and share data connectors with a few clicks.
- Create and share interactive data stories easily with the report builder
- Schedule report updates and enable built-in comments for direct stakeholder interaction.
Reducing maintenance costs
- Reducing the zoo of tools, one platform has all the necessary tools to work together and share results seamlessly
Get a green light from the infosec teams:
- Flexible deployment options that fit organizations’ security standards: on-premises edition that works even in air-gapped environments OR easy-to-launch SaaS offerings
- Ticks all of the boxes with SSO, audit logging, and configuration flexibility
Business Team
Making data-driven teams far more productive together.
- Reducing bottle-neck of Data team for business
- Providing a self-service analytics tool with low-code capabilities. Data teams can provide a data playground to the business teams.
Value proposition for different personas:
Data Science team leads, Data analytics team leads, Head of Analytics, Head of Data, Head of Data Science, VP of Engineering, CTO
Boost the performance of your core data teams by bringing real-time collaboration, a first-class coding experience, and no-code automation to Jupyter notebooks. Enable easy conversation with business stakeholders through the sharing of interactive data apps.
Chief solutions architects, IT Directors, DevOps Managers:
Provide a reliable and scalable data science platform that fits your infrastructure architecture and security standards.
Head of Business functions, Head of Business Intelligence, Head of Analytics:
Get faster data insights, dive deep into existing reports, and perform ad-hoc analytics with SQL cells and no-code visualizations. You can also leverage the flexibility of Python.
C-levels:
Streamline insight delivery and make your data-driven teams far more productive together.
Market
The data analytics market, valued at $241 billion in 2021, is expected to expand to $656 billion by 2030, demonstrating a 13.4% annual growth rate from 2022 to 2029. This growth is driven by the increasing use of data analytics for forecasting, the shift towards cloud services, and the integration of AI and ML in business applications.
Despite potential economic challenges, the demand for data-driven decision-making and efficient technology used to achieve business objectives is likely to increase.
WANT MORE?
Do you want to find out more about JetBrains Datalore and how you and your customers can benefit? Just get in touch to discuss specific requirements.
Email us: nordics@qbssoftware.com
Call us: +46 (8) 55121499