Any business project carries a wide range of risks neglect of which may result in the team being unable to deliver needed results. All data science projects have their specific ones too and it is necessary to be prepared for them. So what are the risks of data science management and how to deal with them?
According to Statista, a global big data market is estimated at around $33.5 billion in 2017. And this number is expected to grow more than twice and reach the value of $72.4 billion by 2022. Therefore, many IT companies launch big data projects and strive to apply data science successfully to business.
However, they often ignore secondary aspects of big data project management and mostly focus on a technical side supposing that risk management is not a big deal. Though, the deep risks knowledge and its detailed management always plays a significant role. In today’s article, we will go through basic risks insights and some things to avoid which may seem to be insignificant at first glance.
Wouldn’t it be great to have a magic formula that safely ensures success of any software development project? – Yes. Unfortunately, it has not been invented yet. What we have today is the following checklist that will help you easily organize a big data management process and avoid fatal mistakes.
We want to deal with this type of risk to come first for a reason. Data theft is the most dangerous thing that can lead to tremendous financial losses. The most damaging data breaches, such as JP Morgan Chase and Evernote cases, have happened within the last five years. The most damaging data breach cases within the last eight years are:
That is why data security has to be #1 priority for each and every DS-project.
In some areas, the law protects data privacy of both computer-based and paper-based types of information. While working on a big data project, professionals have to implement all possible effective mechanisms to ensure data privacy, especially in the healthcare industry.
Needless to say, that data aggregation, collection, storage, and analysis requires huge investments. Therefore, it is very easy to go out of a budget without proper financial planning. Create a strict financial strategy and stick to it during the whole development process.
Data analysis is the most important thing in such projects. One tiny mistake made during processing of a received information can ruin the whole project. That is why project managers have to monitor and control the correctness of data analysis. Estimate carefully every incoming insight so you can implement the best practices and procedures in your development process.
It does not matter how modern and advanced our technologies are or how accurate our approach to information analysis is – the incorrect initial data will always make us go back to the drawing board. Being in a rush, data science (DS) project managers often tend to collect any data first and analyze it later. In such projects, take the lead those who analyze the right data but not the ones who have more data.
We have listed above some of the basic types of risks and now it is the time to go through the main ways of how to easily fail a DS project. The purpose of this paragraph is to help C-level executives see the common mistakes and be more successful in avoiding them.
The basis of any business is not a revolutionary idea, effective marketing strategy, or continuous investments. In fact, the core of any successful project is a team that is responsible for the project completion. On very…very…very rare occasions specialists can work for an idea itself, they all are likely to strive for attractive working conditions. That is why, to hire and hold real data science professionals, ensure they are provided with all the necessary tools and good working conditions to be able to concentrate on your project.
Having 4-7 big data specialists doesn’t necessarily mean that you have a self-organized and automated system. Without a clearly defined leader, one can hardly talk about some plan or project strategy. Everybody will do what they think they have to. A team needs a professional supervisor who would help them follow a common strategy and ensure a timely project delivery. This person is responsible for project management and meeting all deadlines. Hiring a team leader is a must-do investment.
It is hardly possible to imagine such situation but for some companies, it is more than real. A typical DS team structure is as follows: a couple of data scientists, several data engineers, and one team lead. Before even forming a team, managers must learn what skills are required for each type of a specialist in order to hire relevant employees.
We have already talked about risks of big data management and what actions can ruin your DS project. In the following paragraph, we will cover main mistakes every project manager must avoid so as to bring success to the company he works in.
Many DS company owners sure that investing in data infrastructure is a primary task which will provide them with all necessary tools for solving any kind of data science issue. In fact, building a convenient infrastructure and implementing effective management techniques are both required parts of the same process which is a successful DS project development.
Putting an efficient DS team together is always a difficult and time-consuming task that cannot be spoiled just by hiring anyone familiar with a data science theme. It is important for team members to have an expertise in the same area your project is related to. Do not start working on a project until you hire suitable talents.
Even though your team members are good in machine learning and programming, they might be less experienced in turning their insights into meaningful and efficient actions. Setting expectations and estimation of achieved results is the most important metric of continuous progress in building a DS project.
The success of any DS related solution lays within two key points: the right technology and correct management. The second point includes the main its component – a team. It is crucial for a long-term strategy to employ in-house data scientists for a project. Always keep this checklist within reach and it will guarantee your business success.