5 Criteria That Tell You You’re on The Right Path for Data Science

Posted 2018-04-27T Posted by Tom

Data science has the ability to generate incredible efficiencies in your business, identify new revenue streams, or give you an advantage over your competitors, but without the right preparation you could end up getting the wrong results. We have put together a list of five key requirements that will ensure your data science projects have the best chance of success. It is not exhaustive, but if you are a business that is serious about using data science to unlock competitive advantages, meeting these criteria will set you on the right path.

1. Your Questions Are Sharp

Asking broad questions in a data science project won’t deliver profitable results. Instead, you need a clear business objective or problem that needs to be solved. To achieve this, you need to ask relevant and focused questions. Be clear about what you want to gain you’re your data and you will find it easier to recognise what information you need to extract from your data. Whether it is user behaviour, product profitability, streamlining practices or becoming more secure — set the objective first and then find the data to meet it.

2. Your Data Measures What You Care About

Data may seem valuable no matter what it is and to some extent, this can be true. However, to really get the most out of a data science project, the data must measure something that really matters to you or your business. Make sure your data is relevant and your results will be relevant too. You could hold huge amounts of data about the weather, but your business need might be to assess the average buying age for an insurance policy. The weather data may be accurate and even interesting but it is not what your business need is and so it is not beneficial to you. Keep your data relevant to what you care about to ensure that it is of value to you and your business.

3. Your Data is Accurate

You will only obtain accurate results from your data if it is accurate, to begin with. Like a maths calculation, if one of the units is not correct, the whole result will be inaccurate. Data needs to be accurate for you to obtain real, relevant and useful results from it.

A data scientist will help you to assess the reliability of accuracy for the data you hold and will help you to ensure that the demands that you make of it are suitable for the information you have.

4. Your Data is Connected

In order for data science to work, your data must be ‘connected’. Lots of random and disconnected details may seem like a thorough data collection, but unless the information links to each other, its value significantly decreases.

Think of a list of items of clothing that are available for sale and a second list of buyers and their size requirements. Unless you have a third list of the sizes of the clothes available, the data you hold is disconnected and your sale chances are significantly limited.

Holding data that is connected means that a data science project can easily extract relevant information that serves a purpose to very specific business needs. Aim to ensure that you have a full, complete and accurate data reserve to gain the most from data science.

5. You Have a Lot of Data

Of course, data science is nothing if it doesn’t have the fuel to run on — the data. You will only be ready to utilise the possibilities of data science if you have a store of information for it to work from. The greater the amount of data you hold and the more extensive your business need is, the larger your data science projects are likely to be. However, a data scientist will help you to understand what data you have, what this can translate to for your business and what you can be asking from your data.

Having a small amount of data is like having one ingredient for your pizza, you could eat the base alone, but it would not be particularly fulfilling. The more ingredients you add, the better the pizza is and the same goes for data. The more data you have, the more interesting and satisfying the result becomes.

In summary, you need to ensure that your data is relevant, accurate and there is enough of it to warrant a data science project; and you need to have some clear, strategic questions that you want it to answer in order to enjoy the real benefits that data science can offers.

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