Business Data Analytics Goals

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Business Data Analytics Goals – Do you want to create a data strategy but not sure where to start? Don’t get caught in an endless cycle with no destination. The key is to start with a data strategy assessment so you can create a data strategy map to success.

In this blog, we outline what a data strategy assessment is, how to approach it in three steps, and how it ultimately leads to a data strategy map. We also provide examples of successful data strategy assessments.

Business Data Analytics Goals

Successful, thriving companies have one thing in common: they know their data and use it strategically to innovate and drive their business forward. But how do they do it? It’s simple: they have a defined data strategy that serves as the foundation for their data and analytics practices.

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A good strategy is more than data and technology – it’s a defined plan that outlines the people, processes, technology, and data your organization needs to meet your data and analytics goals. A good data strategy addresses exactly what you need to use data more effectively; what processes are needed to ensure that data are of high quality and accessible; what technology enables data storage, sharing, and analysis; and the data needed, where it comes from, and whether it is of good quality.

Before you can answer any of these questions and create a successful data strategy, you must begin with a data strategy assessment.

A data strategy assessment is an in-depth evaluation of the various factors within your organization that affect the quality of your analytics and your ability to make data-driven decisions. During an assessment, you’ll review where you are now, map out where you want to go, and create a plan for how to get there.

The goal: at the end of an assessment, you will have a defined data analytics strategy and a customized, step-by-step roadmap that defines how to implement it. A data strategy roadmap outlines all the steps that need to happen and when, so you can be confident you’re tackling projects in the right order and realizing quick wins right out of the gate.

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There are three steps to a successful data strategy assessment, including identifying goals and challenges, assessing and capturing the current state, and finally, designing a proposed future state. Using this information, you will have what you need to create a road map for a successful data strategy.

A three-step approach to evaluating your data and analytics allows you to understand business goals, capture the current state, and design a future state that will enable long-term success.

Interview IT and business stakeholders to gain a complete understanding of your business goals, current roadblocks, and specific use cases where data can support your goals. During these interviews, discuss and identify:

Get a better understanding of where you are today by assessing your analytics maturity and assessing your current environment through these activities:

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That details where there are gaps within your technologies and skills, explains the need for a new solution, and serves as a benchmark against which progress can be measured.

— outlines the people, processes, technology, and data you need to achieve your goals. During the design process, you will need:

With this information, you can run each item from the future state documentation through an evaluation process based on expected business impact and technical feasibility. This allows you to plan the proposed future state in a prioritization matrix and group everything into projects to determine a logical sequence of activities.

This method allows you to plan projects in the most economical and efficient way (for example, you can combine actions from different quadrants if they are based on a common data entity); In addition, it helps to identify high feasibility/high value projects that need to be initiated so that you can immediately start getting value from your solution.

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A prioritization matrix helps identify high feasibility/high value projects that should kick off data and analytics initiatives.

All the insights and outputs from the first three steps are used to create a data strategy roadmap. The data strategy roadmap is your North Star: it includes a plan, schedule, and costs on how to implement the recommended future state. It prioritizes efforts and identifies quick wins so you can start seeing value immediately, but also includes a long-term plan to increase your analytics maturity.

Once you develop your data strategy roadmap, you can begin implementing the plan because the deliverables outlined in the data strategy assessment are actionable and practical.

We’ve done data strategy assessments for hundreds of clients over the years — each finding value in different, but meaningful ways. Below are some examples of data strategy assessments that have led to successful data strategy roadmaps that enable a better data future.

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Cielo, the world’s largest talent acquisition partner, has goals to continue growing by serving new markets and acquiring additional companies. To do this successfully, Cielo needs to create a data strategy for more effective internal use of data, as well as create a path towards data monetization.

The CareQuest Institute for Oral Health is a national nonprofit organization focused on creating a more accessible, equitable, and integrated oral health system. The organization had recently split from one of their affiliates and had an urgent need to become technologically independent. At the same time, they want to automate and advance their data practices so they can better serve the community.

CareQuest now has complete independence over their data, IT, and security with a future-proof solution that grows with them.

IFit is a leading global provider of interactive connected fitness technology, offering an integrated fitness experience at home, gym, and outdoors. A recent surge in consumer interest has created more opportunities and more data collection, prompting the company to seek outside investment for product growth and expansion. However, to achieve these goals, iFit must analyze customer data to better understand their needs and improve their overall experience.

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This plan gives iFit the basis to be more agile in their market, grow their member base, and improve customer satisfaction.

A data strategy roadmap is the detailed plan you get from going through the data strategy assessment process.

Christina Salmi Christina leads the Data Strategy Service Line, helping our customers think and act strategically around data and analytics.

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If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again. Over the past two years, the interest in data science has grown a lot – I’m sure everyone has heard about the importance of data and data analytics in businesses. According to PwC, there are more than 2.9 million job postings for data science and analytics roles in the US alone. However, many businesses still lack internal data science talent, and as such, must rely on data science consulting firms. But what does data and analytics really mean to businesses?

The main goal of data analytics is to help companies make smarter decisions for better business results. There are four types of analytics –

Analytics. They are interrelated solutions that help businesses make the most of the data they have. Each of these types of analytics offers a different insight. But many organizations don’t know where to start, what the different types of analytics mean and what kind of analytics they need to solve the issues they have or to promote business growth. Therefore, in this article, we will discuss four different types of analytics to understand what each type of analytics provides to improve business performance and operational capabilities. Also, we will outline some steps to consider when looking for a data analytics consultancy to improve the performance and growth of your business.

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This type of analytics analyzes the company’s historical data for insights. Descriptive analytics is used when a business needs to understand the company’s performance on an aggregate level and describe various aspects. The main goal of descriptive analytics is to find the reasons behind the company’s success or failure and learn from past behavior to understand how it affects future results. Simply put, descriptive analytics allows answering the question of what happened?

The most common example of descriptive analytics is the results a business can get from a web server through Google Analytics tools. The results help to understand what actually happened in the past and verify whether a promotion campaign was successful or not based on parameters such as page views. Another example of descriptive analytics can be represented by social media metrics such as the number of followers, likes, reposts, etc. The important point is that

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