How to choose the right data analytics consultancy for your business?
Businesses of all sizes are recognising the transformative power of data analytics. Whether it's uncovering hidden insights, predicting future trends, or prescribing optimal strategies, data analytics has become a cornerstone of modern business strategy. However, many organisations lack the resources or expertise to handle data initiatives in-house. This gap in capability often necessitates the engagement of external data analytics consultancies.
With the proliferation of data analytics solutions and consultancies, choosing the right partner to navigate this complex landscape can be a daunting task.
For many organisations, figuring out what kind of analytics solutions their business needs is the first hurdle. The technical language may be unfamiliar, and the sheer breadth of possibilities can be intimidating. As a result, many businesses struggle to determine which type of analytics they truly need to address their specific challenges and drive growth.
In this blog, we will delve into the intricacies of selecting the ideal data analytics consultancy for your business. From understanding the different types of analytics to evaluating consultancy expertise and offerings, we aim to provide you with practical insights and actionable tips to guide you through the decision-making process.
Let’s begin by exploring the various types of analytics and how they can benefit your business.
Types of data analytics:
There are five primary types of analytics - Descriptive, Diagnostic, Predictive, Prescriptive and Cognitive analytics. These interrelated solutions serve as powerful tools for businesses, enabling them to extract valuable insights from their data. Each type of analytics offers a unique perspective, shedding light on different aspects of your business operations.
1. Descriptive analytics:
This type of analytics analyses the historical data of an organisation for insights. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company or its various functions. The main objective of descriptive analytics is to find out the reasons behind the success or failure of the company and learn from past behaviours to understand how they might impact future outcomes. In essence, descriptive analytics helps answer the question of what happened?
A typical example of descriptive analytics is the data businesses gather from their web servers using tools like Google Analytics. This data reveals what occurred in the past, allowing them to assess for instance, the success of a campaign based on metrics like page views. Similarly, social media metrics such as follower count, likes, and shares serve as another example of descriptive analytics. The key thing to note is that descriptive analytics only show the outcomes—success or failure—without identifying the specific actions that influenced these results.
2.Diagnostic analytics:
Diagnostic analytics provides businesses with detailed insights into specific problems. It involves analysing internal data to uncover the underlying reasons behind success or failure. When sufficient data is available, diagnostic analytics can reveal correlations and relationships across various data sources. For instance, eCommerce leaders like Amazon can scrutinise sales and profits across different product categories to understand why overall profit margins fell short.
Another popular application of diagnostic analytics is within healthcare. It plays a crucial role in identifying how medications affect specific patient segments, considering factors like diagnoses and prescribed medication.
3. Predictive analytics:
Predictive analytics is utilised by businesses to analyse past data patterns and trends, aiming to anticipate future outcomes and optimise ongoing operations. This type of analytics aids in setting realistic goals, effective planning, and managing expectations. It's important to note that while predictive analytics can forecast the probability of future events and outcomes, it cannot predict the future with certainty.
For example, retail giants like Walmart, Amazon, and other retailers leverage predictive analytics to identify sales trends based on customer purchase patterns. They forecast customer behaviour, predict inventory levels, and anticipate which products customers are likely to purchase together. By offering personalised recommendations, they can forecast sales numbers at the end of a quarter or year.
4. Prescriptive analytics:
Prescriptive analytics is a form of advanced analytics that examines data and provides recommendations on the best course of action to take to achieve a specific outcome. It goes beyond descriptive and predictive analytics by not only identifying what has happened or what is likely to happen but also by suggesting what actions should be taken to optimise outcomes. This type of analytics helps businesses make informed decisions by considering various constraints, objectives, and potential outcomes. It essentially answers the question: What should we do next?
5.Cognitive analytics:
Cognitive analytics harnesses advanced technologies like artificial intelligence (AI), machine learning, natural language processing (NLP), and cognitive computing to delve into complex and unstructured data. Unlike traditional analytics, which primarily deals with structured data sets, cognitive analytics focuses on deciphering human thought processes and behaviours by interpreting diverse data sources such as text, images, and audio.
Through cognitive analytics organisations can unveil deeper insights and patterns that might elude traditional analytical methods. By emulating human cognitive abilities, cognitive analytics can grasp context, deduce meaning, and forecast outcomes from extensive and varied data sets. Businesses can transcend mere data analysis, as cognitive analytics aims to comprehend the underlying significance and intentions behind the data. This capability enables organisations to make more enlightened decisions, personalise experiences, and foster innovation across various sectors such as healthcare, finance, customer service, and beyond.
More often than not, businesses need a combination of these analytics solutions to drive results. Unsure of what your unique requirements might looks like? We’re here to help- get in touch with Ei Square today.
For instance, a retail chain may use prescriptive analytics to determine the right balance between carrying excess inventory to meet customer demand and avoiding overstocking to minimize carrying costs. The analytics might suggest adjusting reorder points and quantities based on factors such as seasonal demand patterns, sales promotions, and supplier performance. This leads to improved profitability, better customer satisfaction, and more efficient operations.
How to choose the right data analytics consultancy for your business?
1. Define your problem:
Before you approach a consultancy, you should be able to clearly articulate the specific challenges or goals you want to address with data analytics. This could include improving operational efficiency, optimising marketing strategies, or enhancing customer experience.
2. Gather operational data:
Any reliable data consultancy will always take time to verify whether a client has enough high-quality data before describing the scope of work and negotiating contract terms. Therefore, collect and organise relevant data from your business operations. This could include sales data, customer information, website analytics, or any other data sources that may be pertinent to your problem.
3. Research their expertise:
Look for data consultancies with expertise in your industry and the types of analytics you require. Consider factors such as their track record, experience, and client testimonials.
A good source of information is examining case studies on their website. A case study helps to understand a client's background, the technology, and methods used by a consultancy company to solve a specific problem, along with the outcomes achieved. The level of analytics used in case studies also provides guidance into the client’s level of expertise and knowledge.
4. References & Testimonials:
In addition to reviewing case studies, it's important to research and reach out to previous clients or explore their websites to assess a consultancy's capabilities. It's common to request client contacts for references from data companies you're considering working with. Also, look for marketing materials such as news, press releases, and blog articles, which can provide insight into a specialist's expertise and reputation. Don't forget to check the corporate pages on social media for a more comprehensive understanding of a potential.
5. Evaluation:
Finally, when you have a list of companies you consider suitable experts, it's time to start contacting them. This marks the most crucial part of the consultancy search journey – the evaluation phase. Ideally, this phase involves a meaningful dialogue between the consultancy and the potential client. At Ei Square, we provide a free consultancy service during the discovery stage until the proposal is made. This allows you ample time to decide whether you'd like to work with a data consultancy or not.
Don’t forget!
1. Training: Certain firms, like Ei Square, go beyond traditional consultancy roles. With a team comprising data scientists and engineers, they act as implementers and solution providers. In such instances, it's essential to consider the training they can offer your team. These firms often possess deep expertise not only in analysis and strategy but also in the practical implementation of data-driven solutions. Whether it's domain-specific training, technical training, customised workshops, or ongoing support; having access to a provider with a wide range of expertise can enhance the effectiveness and versatility of your data initiatives.
Curious to learn more? Give us a call to learn more about the value-added serives & training Ei Square can offer alongside project execution.
2. Return on Investment (ROI): The other crucial thing to consider is the return on investment (ROI). It's imperative that the investment made in the solution yields returns within a specific timeframe, aligning with your business objectives and financial goals. Once the consultancy has gathered sufficient information about your problem and data, they should provide you with a detailed scope of work, present preliminary results, and outline the project cost. This allows your team to evaluate the potential benefits against the investment required.
3.Budget:
Budget is a critical consideration when selecting the appropriate data consultancy for your business needs. It's essential to assess the cost-effectiveness of different options, ensuring that the consultancy's fees align with your budget constraints while still providing value for your investment. Look for transparent pricing structures and consider the scalability of services to ensure they fit within your budget both now and in the future. Ultimately, choosing a consultancy that offers a balance between affordability and quality services can help you maximise the return on your investment in data analytics.
Bottom Line:
The key to success lies in thorough research, careful evaluation, and open communication. By taking the time to understand your requirements, assess consultancy capabilities, and establish clear objectives, you can set your business on the path to success in the rapidly evolving world of data analytics.
Equally important is to remember that choosing a consultancy team is not just a transactional decision. They will be your partners in excellence and growth, so it's essential to find a team who are trustworthy and can be transparent and collaborative throughout the process.
Effective communication and mutual trust are paramount. Seek out individuals who can communicate their processes, findings, and recommendations clearly and openly.
As you embark on this journey, remember that you're not alone. With the right knowledge and resources at your disposal, you can harness the power of data analytics to fuel innovation, drive growth, and stay ahead of the competition.
What sets Ei Square apart is our approach to client collaboration. We can work both independently to your existing data team or as an extension of them complementing their skills and expertise. Book a discovery call with us to learn more.