Data Analytics

Data Analytics

Organisations now have greater access to data and more strategies to use it than before. They just have one thing going for them: time. Experts in AI, data, and analytics strategy build systems and organisations that turn opportunities into value. Fast.

Data is all around us, flowing through an ever-expanding network of sensors, apps, and business models. Companies that use data and analytics to revolutionise what they do and how they do it are far less prevalent. Transforming your firm into a data-driven one takes time and effort. It requires not only a vision, but also the necessary data assets, data governance, talent, and culture. It necessitates the breakdown of data silos, the adoption of new working practises, the learning of new skills, and, in some cases, the acceptance of new mindsets. It needs a constant focus on business outcomes.

Methodology for Data and Analytics

When used in the right context and with the right enablers, data and analytics can help you grow. By establishing strategic, technical, and human capabilities that take companies from vision to value, we help them become genuinely data-driven bionic organisations.

DATA & DIGITAL PLATFORM

One of the most significant hurdles for many firms is their own Infrastructure. Legacy systems, fragmented technology, and data that was localised—and difficult to share—hampered data and analytics plans. Few businesses can afford to wait years to adopt new IT infrastructure. There is no such thing as a corporation that can afford to do nothing and sit on its hands.

To accelerate data-driven change, we build a data and digital platform. It acts as a hub for data and analytics, detaching data from legacy systems so that data can be accessible rapidly for new applications and business models. A data and digital platform enables firms to think big, start small, and develop swiftly without being bound by existing IT infrastructure or its renewal rate. With the help of more than 400 certified platform professionals, we help customers expand their organisation—their people, processes, and culture—in ways that allow them to realise the full potential of data and analytics.

TOOLS DEPLOYED IN DATA ANALYTICS

1. Microsoft Power BI

2. SAP BusinessObjects

3. Sisense

4. TIBCO Spotfire

5. Thoughtspot

6. Qlik

7. SAS Business Intelligence

8. Tableau

9. Google Data Studio

10. Jupyter Notebook

11. IBM Cognos

12. Oracle Analytics Cloud

13. R

14. Python

15. Excel

Use Data Enablers to help you develop your data strategy.

“Data only has value when it has a function”

Initial raw data uses our Data Strategy Model, which is built on the data enablers that are required to build a data-centric organisation. Because data science is their entire emphasis, many businesses struggle. Further investigation exposes a lack of data to analyse or haphazard decisions that aren't always advantageous to your company's goals. It's crucial to realise that all six data enablers are interconnected. If you don't have a common view of your customer data and no data quality monitoring on the data set, how can you even begin to get this 360-view of your customers that everyone is talking about?

Initial raw data uses our Data Strategy Mode

1.Business Ownership.

This is the most crucial and initial step. There is no need to continue if you do not own a business. Because your strategy should constantly be linked to business goals, the company should take ownership of the data strategy so that it isn't just supported by IT or the data management department. Not merely because "we need to do something with data," the data strategy is in place to help the firm succeed.

2.Technology

Excel can manage your information models, generate monthly reports, and keep your data quality dashboards up to a point, but if you actually want to become more data centric/driven, you will surely need additional tools and technologies.

3.Tactical and Operational Boards.?

To manage the change, (strategic) boards with representation from data leadership must be established. Decisions must be made, and the governance mechanisms that will be used to make those decisions must be designed in advance. These boards will assist the company in making a smooth and continuous transition to a data-centric one.

4.Processes.

Cross-functional procedures are essential, especially when attempting to create a data-centric organisation. From senior executives to junior analysts, all staff require structure and guidance in terms of what they can and cannot accomplish.

What Role Do Data Analytics Consultants Play in Long-Term Business Success?

Although data analysis is a complex process, it fundamentally breaks down to making informed judgments based on datasets with important commercial ramifications.

Data analytics has moved from simple Excel analyses of past events to complex analytics that use algorithms and machine learning to find trends and predict outcomes. Prescriptive analytics strives to help businesses extract more value from their data by making suggestions based on found patterns.

Many organisations are discovering that combining and analysing existing first-party data (data they already control) with third-party data (data from other sources) using data science and artificial intelligence can help them better monitor and forecast future customer behaviour patterns (AI). Companies who use internal and external data to provide timely signals and comprehensive insights outperform their competition by double digits, according to ThoughtSpot.

Companies aren't just buying the latest "shiny object" when they invest in data analytics. They're successfully transforming their business by equipping themselves with the appropriate tools, the most comprehensive datasets, and the knowledge they need to uncover actionable insights that will help them grow and achieve a competitive advantage

Capabilities of Data Analytics?

1. Develop skills within the organisation

Retrieving significant insights from data requires a high level of understanding over time, making it a behind-the-scenes job carried out by a select few. Data visualisation, on the other hand, is becoming a levelling force in data analysis.

Rather than having two separate teams, one trained in a certain industry and the other in statistics and analysis, self-service analytics is now possible thanks to modern tools. Workers will have instant access to their information, as well as the capacity to discover and share its economic value.

2. Gain insight without writing a single line of code

There's no denying that code is the foundation of data analysis. However, many workers have found it difficult to get meaningful insights as a result of this. Anyone, whether a seasoned developer or not, may benefit from this information thanks to data visualisation.

Visual analytics tools assist all users in extracting value from their data.

3. Reduce the time it takes to gain understanding.

It goes without saying that in order to compete, modern firms must be able to act quickly, gathering useful insights faster than ever before. This agility is provided by visual analytics, which makes it easier for businesses to get the data they need to make strategic decisions.

4.Choose the correct data - Small data is a good place to start.

Data collection and analysis are continually expanding, and organisations must understand what data is already available, the quality of that data, and which data they must select. Starting with small data' is frequently the most successful strategy to picking relevant data?

since it simplifies the process and provides an early indicator of the quality of the larger data. Starting with little amounts of data can help businesses avoid data redundancy.

5.Streaming analytics

In real time, integrate, process, and analyse event streams to make data more relevant and accessible as soon as it's generated. By mixing streaming and batch data analysis, Dataflow creates logical data pipelines. Every second, Pub/Sub consumes hundreds of millions of events.

6.Geospatial analytics & AI

With a uniquely powerful, full–platform solution built on category-leading Google technologies like BigQuery, Earth Engine, Google Maps Platform, and more, rethink how geospatial relationships and insights may help you create a more successful and sustainable future for your organisation.

Trends In data Analytics

1. Switch to the Cloud

Previously, data warehouses were kept in physical storage facilities. However, in the last year, a number of businesses have moved their data warehouses to the cloud or opted for a hybrid solution that combines cloud and on-premise warehouses. By 2022, public cloud services will be required for 90% of data and analytics innovation, according to Gartner.

In terms of creativity, speed, and adaptability, cloud computing is making strides. Most vendors provide end-to-end data warehousing, allowing your company to profit from data analytics right now and become more agile.

2. Composable data & analytics

Customers can combine components from a variety of data, analytics, and AI solutions to create a flexible, user-friendly, and accessible experience using composeable data and analytics. Executives would be able to link data insights to business activities as a result of this.

The rise of composable data and analytics is expected to promote cooperation, improve analytics access, and dramatically improve a company's analytics capabilities.

3. Predictive analytics

Customers can employ composable data and analytics to create a flexible, user-friendly, and accessible experience by combining components from a variety of data, analytics, and AI technologies. Executives will be able to link data insights to business activities as a result.

The rise of composable data and analytics is expected to strengthen a company's analytics capabilities through fostering cooperation, improving analytics access, and dramatically increasing analytics capabilities.

4. Customer personalization

Not only did the year 2020 bring about a shift in the workplace, but it also brought about a shift in consumer behaviour. As a result of the COVID-19 pandemic, more people started buying products and services online, leading many businesses to go digital.

Digitalization, on the other hand, brought with it more data and improved customer insights. More companies are anticipated to focus on providing highly customised experiences to their customers in the coming year. To put it another way, businesses will need to create a data-driven "Personalised Client Experience Plan" in order to give the appropriate offer to the right consumer at the right time.