Data analysis services

What we offer

4 methods of data analysis

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4 methods of data analysis

Data analysis services cover a large gamut of operations that relate to collecting, processing, and presenting business information in a convenient format so that companies can leverage it as actionable insights in their strategic planning, decision-making, and performance optimization. As a data analysis company with over ten years of experience in data analytics, DICEUS meets our clients halfway through providing the following kinds of services.

Audit of data management/governance

Most organizations have some data management policy, but they may not be sure it is up to the mark. We perform a complete check of your firm’s procedure, gauge its efficiency, detect bottlenecks and pitfalls that impede its smooth employment, and pinpoint future risks likely to crop up if you follow the adopted data handling strategy.

Reports on audit and analytics results

The outcome of the auditing services provided by our specialists is a comprehensive report. This document lists systemic data issues, reveals incomplete data sets and siloed data, exposes data security gaps and barriers to data access, and identifies insufficient depth or width of data collection to enable our customers to institute corrective measures.

On-time delivery of results and recommendations

Knowing the field inside out, we do not only deliver the results of the IT data analysis conducted within the timeframe stipulated by the contract. Our experts also offer relevant and benchmarking-driven recommendations that will allow businesses to step up data quality, increase the efficiency of data governance, and support compliance adherence.

Guaranteed security of your data

When a business or personal data stored by the organization on-premises or in the cloud is compromised, the company may suffer substantial financial losses and severe reputational damage. Realizing the vital importance of data security, DICEUS employs exclusive protective measures while doing any tasks and jobs related to processing corporate data.

ETL, QA, and other data management services

The AI-powered data analysis solutions that we employ enable versatile operations with information. Our ETL (Extract, Transform, Load) tools are honed to gather data in various formats from multiple sources and unite it into a single database. In addition, QA services by DICEUS guarantee the consistency, durability, accuracy, and compatibility of the data we process.

Data warehouse (DWH) development

Having all data hoarded in disparate locations causes a problem with its efficient handling. We can bring it together under one roof of a data warehouse. It relies on top-notch back-end software, infrastructure, and security protocols and has a customizable and user-friendly dashboard at the front end, which turns the data processing routine into a cakewalk.

Benefits of DICEUS data consulting

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Benefits of DICEUS data consulting

Efficient data analysis is the cornerstone of business success for enterprises in any industry. However, even for many large-scale brands (to say nothing of cash-strapped startups and small companies), investment in developing and managing analytics tools is an expenditure item far beyond their modest means.

By outsourcing such services to seasoned data analysis firms – and DICEUS is one of the leaders in the niche – businesses can streamline and facilitate data management and analytics. It is a surefire recipe for enhancing the efficiency of their workflow, expanding market reach, and stepping up customer satisfaction of the clientele.

Benefits of our data consulting

Organizations amass tons of client and internal data during their operation. Yet without a competent helping hand, they often struggle to turn this data into a corporate asset. The comprehensive data analytics and consulting services DICEUS provides will allow your company to become an efficient insight-driven brand. What makes us so sure?

More than 12 years of data analysis experience

An IT company may have the most competent staff, but being greenhorns in some fields, they are unlikely to bring maximum value to an enterprise that needs assistance in a particular sphere. The mavens of DICEUS have over a dozen years of rendering data analysis services to startups and major-league business players, which serve as a solid store of knowledge to draw upon.

Strong expertise in over 10 industries

The workflow and target audience of organizations from different business realms has their specifics and unique challenges to address. Our company has cooperated with insurance, retail, logistics, fintech, banking, healthcare, construction, and other enterprises, so we understand the peculiarities of their functioning and can adapt to meet their needs, goals, and growth opportunities.

Data science expertise

Possessing general competence in the IT domain is no guarantee that a software vendor will handle data analysis on a proper level. Data science experts of DICEUS have profound technical skills (programming, data visualization, and managing databases), which, combined with such soft skills as collaboration and public speaking, ensure the best possible outcome.

130 project accomplished

This is again about the experience. The quality of any service or product a company specializes in is proven by the successful use cases it can display. We have over 130 accomplished projects under our belt where we delivered high-end solutions, rendered expert services, and provided competent consulting, leaving our clients completely satisfied with our input and cooperation approaches.

How it works

An experienced team with solid competence in the field and the state-of-the-art tech stack to apply are the two vital summands of success in data analysis. However, no less critical is an adequately organized procedure for performing it. Vetted mavens of DICEUS have developed a universal roadmap that manifested its efficiency in numerous use cases.

Defining the project scopeThis discovery stage lays the foundations for successful running and completing data analysis. Closely cooperating with all stakeholders, our team establishes project goals and objectives, gathers project requirements, identifies and allocates necessary resources, assesses constraints, creates exclusions, defines deliverables, and develops a change control pipeline.
Collecting and exploring dataNow we get down to the procedure’s core, hammer and tongs. First, our experts get access to all corporate data sources (websites, ERP and CRM systems, IoT devices, on-premises and cloud databases, etc.) and assemble assets in one virtual venue. After that, they leverage techniques to examine the collected data in depth and detail thoroughly.
Analyzing and cleansing dataOnce the data is explored, we expose patterns, trends, outliers, and anomalies within datasets. The best data analysis practices utilized by our specialists rely on the most advanced analytical and visual tools to do it. Also, they cleanse the data, which involves fixing incorrect, duplicate, corrupted, or fallaciously formatted data, updating it if possible, or removing the flaws.
Developing frameworks/data modelsAfter the proper analysis, data has to be treated and structured to make it usable for various purposes. We create data models that embrace a conceptual representation of data and reveal relationships between multiple items in the datasets. Finally, our specialists visualize it in our client’s most convenient format (texts, diagrams, charts, etc.).

What impacts your project duration

With the arrival of new data, its analytics can become a never-ending procedure. However, companies want to know how long the analysis of their available data will last. To give the exact timeframe, we need to know:

  • Project requirements
  • Anticipated deadlines
  • Team size and roster
  • Chosen platforms and tech stack
  • Change requests

What affects your project costs

Modern entrepreneurs are calculating people who want to get all value for the money they pay for any service. We can announce the precise sum only if we are aware of the following:

  • Harnessed technology
  • Project scope and complexity
  • The urgency of project completion
  • The engagement model the customer prefers
  • Current data condition

What we need from your side

An organization that commissions the job and forgets about the project expecting the outsourcer to do everything independently, can hardly hope to get a fast and high-quality outcome. Therefore, to achieve a satisfactory result for both parties involved, we need close cooperation from your side, which includes providing the following information.

  • The project vision, goals, and roadmap (if they exist)
  • High-level project requirements
  • Available project documentation (software architecture, mockups, or any other papers)
  • Project deadlines
  • The client’s accessibility schedule (as a rule, several hours a week to hold requirements gathering sessions)

Our tech stack

Explore our case studies

Frequently asked questions

What is the data analysis?

The process of working with data to obtain valuable insights will serve as a foundation for more knowledgeable decision-making. It involves collecting raw data from multiple sources, storing it in one virtual place (preferably a cloud-based data warehouse), applying various tools to examine it, and presenting the obtained results in an easy-to-understand format.

What are the key methods of data analysis?

Four primary data analysis methods differ in their purpose. The descriptive method aims to reveal what happened in the past. The diagnostic method is called to explain why it happened. The predictive method maps out the developments that are likely to occur in the future. The prescriptive method offers the best course of actions to take.

How much does data analysis cost?

Any digitally-driven endeavor is never a chump change issue; data analysis is no exception. Companies allocate from 2% to 6% of their budgets for data analytics, ranging from $10,000 to $100,000 annually for small and mid-size businesses. Many factors condition the final sum, the chief of which are the volume of data to analyze and the tech stack to employ.

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